The Power of Discourse: Associations between Trainers’ Speech and the Responses of Socialized Wolves and Dogs to Training

Abstract

In a previous study, we found that Positive Reinforcement Training reduced cortisol of wolves and dogs; however, this effect varied across trainer–animal dyads. Here we investigate whether and how the trainers’ use of speech may contribute to this effect. Dogs’ great interest in high-pitched, intense speech (also known as Dog Directed Speech) has already been reported, but whether and how wolves respond similarly/differently to voice characteristics has never been studied before. We analyzed 270 training sessions, conducted by five trainers, with nine mixed-breed dogs and nine wolves, all human-socialized. Through Generalized Linear Mixed Models, we analyzed the effects of (a) three speech categories (nice, neutral, reprehensive) and laugh; and (b) acoustic characteristics of trainers’ voices on animals’ responses (correct responses, latency, orientation, time at less than 1 m, non-training behaviors, tail position/movements, cortisol variation). In both subspecies, tail wagging occurred more often in sessions with longer durations of nice speech, and less often in sessions with reprehensive speech. For dogs, the duration of reprehensive speech within a session was also negatively related to correct responses. For wolves, retreat time was associated with more reprehensive speech, whereas duration of nice speech was positively associated with time spent within one meter from the trainer. In addition, most dog behavioral responses were associated with higher average intonations within sessions, while wolf responses were correlated with lower intonations within sessions. We did not find any effects of the variables considered on cortisol variation. Our study highlights the relevance of voice tone and speech in a training context on animals’ performances and emotional reactions.

Full text

Citation: Fonseca, M.G.B.; Hilário,
H.O.; Kotrschal, K.; Range, F.; Virányi,
Z.; Duarte, M.H.L.; Pereira, L.C.G.;
Vasconcellos, A.d.S. The Power of
Discourse: Associations between
Trainers’ Speech and the Responses
of Socialized Wolves and Dogs to
Training. Animals 2023,13, 1071.
https://doi.org/10.3390/ani13061071
Academic Editors: Heidi Lyn, Joclyn
Villegas and Miriam Annika Vogt
Received: 6 January 2023
Revised: 6 February 2023
Accepted: 11 March 2023
Published: 16 March 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
animals
Article
The Power of Discourse: Associations between Trainers’ Speech
and the Responses of Socialized Wolves and Dogs to Training
Melissa Gabriela Bravo Fonseca 1, Heron Oliveira Hilário 2, Kurt Kotrschal 3, Friederike Range 4, Zsófia Virányi 5,
Marina Henriques Lage Duarte 1,6 , Laryssa Cristina Gomes Pereira 1and Angélica da Silva Vasconcellos 1, *
1Program of Post-Graduation in Vertebrate Biology, Pontifical Catholic University of Minas Gerais,
Belo Horizonte 30535-901, Minas Gerais, Brazil
2Laboratory of Conservation Genetics, Pontifical Catholic University of Minas Gerais,
Belo Horizonte 30535-901, Minas Gerais, Brazil
3Department of Behavioural and Cognitive Biology, University of Vienna, 1010 Vienna, Austria
4Domestication Lab, Konrad Lorenz Institute of Ethology, University of Veterinary Medicine Vienna,
Savoyenstraße 1a, 1160 Vienna, Austria
5Messerli Research Institute—University of Veterinary Medicine Vienna, Medical University of Vienna,
University of Vienna, 1210 Vienna, Austria
6Bioacoustics Laboratory, Museum of Natural Sciences, Pontifical Catholic University of Minas Gerais,
Belo Horizonte 30535-901, Minas Gerais, Brazil
*Correspondence: angelicavasconcellos@gmail.com; Tel.: +55-031-99940-6015
Simple Summary:
In a previous study, we found that Positive Reinforcement Training promoted
relaxation in wolves and dogs. Here, we investigate aspects of the trainers’ voices possibly involved in
this effect. Dogs’ great interest in high-pitched, intense speech has already been reported, but whether
and how wolves respond similarly/differently to voice characteristics has never been studied. We
analyzed 270 training sessions with nine mixed-breed dogs and nine wolves. We grouped human
speech into three categories: nice, neutral, and reprehensive, and analyzed how their duration and
their acoustic characteristics within training sessions were associated with animals’ behavior and
physiology. The longer the duration of nice speech during a session, the more often tail wagging was
observed in both subspecies, while the opposite was found for reprehensive speech. The duration
of reprehensive speech was also associated with a decrease in correct responses in dogs and with
retreating in wolves, while a longer use of nice speech was associated with animals being next to the
trainer for longer within a session. Sessions with a higher average pitch was more often associated to
changes in dog behavior, while wolf behavior changes were more often associated to low intonations.
Our results suggest that a friendly voice during training supports performance and positive emotional
responses in wolves and dogs. The different response towards the pitch between the subspecies
may be related to the domestication process, which selected, in dogs, characteristics that facilitate
interaction with humans.
Abstract:
In a previous study, we found that Positive Reinforcement Training reduced cortisol of
wolves and dogs; however, this effect varied across trainer–animal dyads. Here we investigate
whether and how the trainers’ use of speech may contribute to this effect. Dogs’ great interest in high-
pitched, intense speech (also known as Dog Directed Speech) has already been reported, but whether
and how wolves respond similarly/differently to voice characteristics has never been studied before.
We analyzed 270 training sessions, conducted by five trainers, with nine mixed-breed dogs and nine
wolves, all human-socialized. Through Generalized Linear Mixed Models, we analyzed the effects of
(a) three speech categories (nice, neutral, reprehensive) and laugh; and (b) acoustic characteristics
of trainers’ voices on animals’ responses (correct responses, latency, orientation, time at less than
1 m, non-training behaviors, tail position/movements, cortisol variation). In both subspecies, tail
wagging occurred more often in sessions with longer durations of nice speech, and less often in
sessions with reprehensive speech. For dogs, the duration of reprehensive speech within a session
was also negatively related to correct responses. For wolves, retreat time was associated with more
reprehensive speech, whereas duration of nice speech was positively associated with time spent
Animals 2023,13, 1071. https://doi.org/10.3390/ani13061071 https://www.mdpi.com/journal/animals

Animals 2023,13, 1071 2 of 23
within one meter from the trainer. In addition, most dog behavioral responses were associated
with higher average intonations within sessions, while wolf responses were correlated with lower
intonations within sessions. We did not find any effects of the variables considered on cortisol
variation. Our study highlights the relevance of voice tone and speech in a training context on
animals’ performances and emotional reactions.
Keywords: canids; human-animal interactions; welfare; behavior; acoustics analysis
1. Introduction
Because humans are extremely communicative, any species that cohabits with us was
likely selected to take advantage of this characteristic [
1
]. Dogs (Canis familiaris) are a
good model for studying interspecific communication because they can develop and use
a flexible signaling system when dealing with humans [
2
]. Zimen [
3
] showed that this
capacity is derived from wolves. Both subspecies use visual and acoustic signals; however,
many canine visual signals involve tail and/or ear movements, which have no counterpart
in humans, making the understanding and use of such signals more challenging for us.
Likewise, human visual cues often include hand movements for which there is no canine
parallel. With regard to acoustic signals, some can result in a lasting communicative pattern
based on repeated interactions between sender and receiver [
4
] and, in the case of dogs,
this ability was thought to have been intensified through domestication [
5
]. Studies have
pointed to a synergy between phylogeny and ontogeny in the development of dog abilities,
including interspecific communication [6].
Considering that humans are a very vocal species, it is plausible that the commu-
nicative approach towards dogs is spontaneously based on such a channel. In Western
cultures, it is common for humans to adopt a special type of speech when they “talk” to
pets [
7
], the pet-directed speech (PDS). It shares some of the acoustic characteristics of
infant-directed speech (IDS), including an increase in pitch and vowel articulation, in an
exaggerated and affected manner, as well as the decrease in the rhythm of words compared
to adult-directed speech (ADS). PDS and IDS may show similarities because both dogs
and babies are non-verbal listeners, and the affective bond between owners and dogs are
known to reflect the human parent–baby bond [
8
]. In addition, both owners and dogs have
been shown to experience oxytocin secretions after a brief period of petting [
9
], and a study
highlighted brain activation of connected areas when mothers viewed images of both their
child and their dog [
10
]. Furthermore, it has been shown that the acoustic characteristics of
PDS attract the attention of dogs significantly more than the ADS [8].
Ben-Aderet and colleagues [
11
] were the first to investigate both the production of
speech used specifically for dogs (Dog Directed Speech, DDS), and the behavioral responses
of puppies, adult, and old dogs to DDS. They found that although humans produce
DDS for animals of different age groups, the preference of dogs for this kind of speech
decreases with age: puppies showed greater behavioral responses to DDS than to ADS,
while adult and old dogs showed no preference for either type of speech. For the authors,
targeting DDS to adult and old dogs may simply constitute a “spontaneous attempt to
facilitate interactions with non-verbal listeners”. This interpretation may be related to
the “hyperphony” hypothesis [
12
], according to which broadcasters use optimized speech
patterns to improve speech intelligibility with animals, which are expected to be more
sensitive to this special modulation of the voice tone.
However, alternative explanations for the lack of effect of DDS on adult dogs still
deserve investigation. Ben-Aderet and colleagues [
11
] suggested that adult dogs may need
additional cues (e.g., gestures) to respond to unfamiliar speakers. In their initial study, DDS
and ADS were recorded, and only their playbacks were used in the experiments. Therefore,
adult dogs did not have the opportunity for receiving extra cues. This condition may
have affected communication by influencing sound reception, and precluding dogs from

Animals 2023,13, 1071 3 of 23
having social interaction; therefore, they may have not seen any social benefit of reacting
preferably to any kind of speech. In contrast, puppies, having little experience in terms
of environment and social interactions, may lack focus on this aspect, responding to DDS
even in the absence of a physical experimenter.
Taking this matter as a starting point, Benjamin & Slocombe [
13
] carried out an
experiment to investigate the possible effects of DDS and ADS on the levels of attention
and affiliation of adult dogs. The authors also investigated whether possible behavioral
preferences were modulated by prosody and/or content, under conditions ecologically
more relevant compared to the study by Ben-Aderet and colleagues [
11
]. Adult dogs
attended more and sought more proximity to an experimenter whose speech had dog-
relevant content (e.g., good boy/girl!) and was spoken with elevated pitch and exaggerated
prosody than to the experimenter whose speech lacked any of these characteristics. This
finding suggested that DDS might fulfill a dual function: improving attention and social
connection. This last aspect is in line with the current understanding reached by research
with children, which suggests that not only is IDS crucial for the development of meaningful
social relationships with caregivers [
14
], but that it is useful for facilitating language
acquisition [
15
]. Another study has corroborated the results of Benjamin & Slocombe [
13
]
by pointing out that dogs have shown preferences towards a target object associated with
DDS in the DDS versus ADS condition [
16
]. Despite the existence of studies investigating
the perception and processing of human vocalizations by dogs, little is known about how
their ancestors, the wolves, respond to these stimuli.
In line with the fact that DDS is often characterized by high pitch [
7
], Ben-Aderet and
colleagues [
11
] also found that dogs reacted more to high pitch than low pitch. Interestingly,
however, pitch did not seem to determine their ability to differentiate between DDS and
ADS nor did it explain preferences for DDS [
16
], questioning the importance of single
acoustic parameters.
We here focus on communication between humans and animals during Positive
Reinforcement Training (PRT), a type of Operant Conditioning technique, which was
shown to promote improvements in animal welfare, possibly through the association
between the animals’ behavior and its pleasant consequences [
17
,
18
]. In essence, PRT may
benefit animals by providing positive feedback and, eventually, by promoting opportunities
for the animal to exert some control over their environment [
19
]. However, the beneficial
effects recorded with PRT might also be promoted by the opportunity to interact with a
familiar person with whom the animal may have developed a social bond [20].
Vasconcellos and colleagues [
18
] investigated the effects of regular interactions of
equally raised and kept timber wolves and mixed-breed dogs with familiar humans during
PRT sessions. In addition to their training performance, the animals’ behavioral and
physiological responses (salivary cortisol variations) were evaluated. Apart from a fine
training performance, a reduction in salivary cortisol concentrations was recorded in both
subspecies, as well as low rates of non-training-related behaviors (NTBs) [
21
]. Interestingly,
up to 22.8% of the variation of the animals’ responses were due to trainer identity. These
results showed, for the first time, that not only dogs but also wolves may benefit from PRT.
Considering that the way humans communicate/interact with dogs has been shown
to affect the owner-dog relationship, animal behavior, and possibly animal welfare [
22
],
in the current study, we explored how animals’ responses might differ in accordance to
trainer communication style within a training session. We reasoned that one important
point might be the use of voice and thus set out to evaluate the effects of the duration
and frequency (number of occurrences) of types of speech used within a training session,
and the average acoustic characteristics of trainers’ voices during sessions on animals’ re-
sponses. We focused on two hypotheses: (1) the communication style (duration/frequency
of nice, neutral or reprehensive speech) and specific acoustic parameters (such as pitch)
that characterized the different speech types are associated with different behavioral and
physiological responses of dogs and wolves during training; (2) the domestication process
affected the perception and responses of animals to the communication style of humans

Animals 2023,13, 1071 4 of 23
and acoustic sound parameters. From these hypotheses, we developed two predictions:
(1) the duration/frequency of “nice” speech and/or high pitch will be associated with
behaviors indicative of affability and interest in training, such as increased duration of tail
wagging, attention and performance in the animals, and greater proximity of the dyad,
whereas (2) the duration/frequency of “reprehensive” speech will be associated with an
increase in behaviors unrelated to training, greater distance in the dyad, and duration of
the tail being retracted for both subspecies.
2. Materials and Methods
2.1. Ethics Statement
All study animals were kept at the Wolf Science Center (www.wolfscience.at: license
n
◦
: AT00012014). The CITES (www.cites.org) import permits for the animals are 2008:
Zoo Herberstein, Austria: AT08-B-0998, AT08-B-0996, AT08-B-0997; 2009: Triple D Farm,
USA: AT09-E-0018; 2012: Minnesota Wildlife Connection, USA: 12AT330200INEGCJ93. The
animals were housed in accordance with the Austrian Federal Act on the Protection of
Animals (Animal Protection Act—TSchG, BGBl. I Nr. 118/2004). Hence, in accordance
with the Austrian Animal Experiments Act (BGBl. I Nr. 114/2012, Tierversuchsgesetz
2012—TVG 2012), no ethical approval was officially required, but we still obtained one
from the University of São Paulo, Brazil (Committee of Ethics for Animal Research from
the Institute of Psychology, University of São Paulo, Brazil, approval number 016.2009).
2.2. Subjects
Eighteen animals were studied: nine timber wolves (mean age = 15 months
±
2.04) and
nine dogs (mean age = 21 months
±
3.37), all born in captivity, raised and kept following
the same protocol [
23
] at the Wolf Science Center, an institution located in the Game Park
Ernstbrunn, Austria. Table 1shows the animals’ names, sexes, and ages at the onset of the
study.
Table 1.
Names, sexes *, and ages (in months, at the moment of data collection) of the dogs and
wolves that participated of Positive Reinforcement Training sessions in this study.
Wolves Age (Months) Dogs Age (Months)
Apache (M) 11 Alika (F) 12
Aragorn (M) 24 Kilio (M) 11
Cherokee (M) 11 Layla (F) 24
Geronimo (M) 11 Maisha (M) 11
Kaspar (M) 24 Meru (M) 43
Nanuk (M) 11 Nia (F) 25
Shima (F) 24 Nuru (M) 26
Tatonga (F) 11 Rafiki (M) 12
Yukon (F) 11 Zuri (F) 26
* M = male, F = female.
All study animals were hand-raised and maintained in close contact with the five
participating trainers during the first 20 weeks of life. From the third week onwards,
sporadic contact with conspecifics (including adult individuals, wolves in the case of wolf
pups and dogs in the case of dog pups) were provided, while at this age we also started
with more formal PRT interactions.
2.3. Saliva Collection
Prior to the beginning of the study, dogs and wolves were trained with the use of PRT
techniques to allow saliva collection. Saliva was used for the physiological assessment of
stress via measuring salivary cortisol. The collection procedure included the introduction of
two surgical hydrocellulose sponges (Sorbette, by Salivette®) in the animal’s cheek pouch
for sufficient time to get these soaked. Immediately after saliva collection, the sponges

Animals 2023,13, 1071 5 of 23
were transferred to a plastic tube (Sarstedt
®
) and stored at
−
20
◦
C until analysis by enzyme
immunoassay [
18
]. During the sessions of familiarization with the collection procedures,
as well as during saliva collection and the training sessions, the animals were rewarded
exclusively with pieces of Gouda cheese, to control for the influence of protein in the saliva
samples [
24
]. Saliva collection was performed 2–4 min before, and 15 min after the end of
each training session.
2.4. Procedures
Each study animal participated in 15 training sessions of 5 min each (3 sessions with
each of the 5 trainers), totaling 270 sessions, 135 with dogs and 135 with wolves. The
training sessions were run between May 2010 and March 2011 and were filmed with a
video camera. These were conducted with the animals isolated from the pack, between
8:30 a.m. and 5:00 p.m., in a training room (63.6 m
2
) located close to their enclosure. The
training room did not allow visual contact with other animals or humans and was almost
empty except for a raised platform on one side. No animal participated in more than one
training session per day. Each trainer, however, worked with four to nine animals on each
training day, in a randomized order.
All wolves and dogs were already familiar with the cues used during the training
sessions (sitting, laying down, turning around, walking around the trainer, giving a paw,
allowing the placement of a muzzle, allowing the placement of a harness, rolling, standing,
staying, and looking into the trainer’s eyes). The vocal interactions during the sessions
occurred in a naturalistic context. Trainers were instructed to remain standing, in a relaxed
posture, emitting cues in random order and rewarding the animals when the cues were
correctly followed. Animals’ responses were rewarded with cheese in a continuous rein-
forcement regime. The study was developed to be minimally invasive, in such a way that
every animal participated voluntarily in the training sessions, being invited by name to
enter the test room. In case of reluctance or discomfort with the procedure, the animal
would be reintroduced to the group, where it remained until it was ready to make part of
the session.
2.5. Animals’ Behaviors and the Types of Speech
We evaluated all trainer vocalizations: speeches (phrases uttered by the trainers
during the interactions), animal names, and laughs produced by the trainers during the
270 five-minute training sessions
, excluding the commands used for training. The com-
mands were not analyzed because their pronunciation was standardized, always in a
neutral tone of voice. The start and end of each vocalization were selected both acoustically
(by listening to the records), and visually, with the use of spectrograms (Figure 1).
Table 2contains the description of seven types of trainer vocalization recorded in
the videos from the sessions, whose duration or frequency of use was summed up for
each training session and considered here as explanatory variables: nice, neutral and
reprehensive speeches and names; and laugh. As response variables, we used the same
ones evaluated by Vasconcellos et al. [
18
]: correct responses, latency, visual orientation
to trainer, time at less than 1 m from trainer, Non-Training Behaviors (NTB; representing
counter-productive behaviors, regarding training), and cortisol variation, in addition to the
animals’ tail position/movements (Table 3).

Animals 2023,13, 1071 6 of 23
Animals 2023, 13, x 6 of 24
Figure 1. Illustrative spectrogram of a trainer’s speech. Blue boxes represent the marking of the
fundamental frequency (F0) of two distinct speech events. Arrows indicate the maximum (max.)
and minimum (min.) frequencies of these speeches. X axis: time interval (200 milliseconds); y axis:
frequency in kHz.
Table 2 contains the description of seven types of trainer vocalization recorded in the
videos from the sessions, whose duration or frequency of use was summed up for each
training session and considered here as explanatory variables: nice, neutral and
reprehensive speeches and names; and laugh. As response variables, we used the same
ones evaluated by Vasconcellos et al. [18]: correct responses, latency, visual orientation to
trainer, time at less than 1 m from trainer, Non-Training Behaviors (NTB; representing
counter-productive behaviors, regarding training), and cortisol variation, in addition to
the animals’ tail position/movements (Table 3).
Table 2. Description of seven types of trainer vocalization, obtained for each training session with
socialized dogs (Canis familiaris) and wolves (Canis lupus).
Parameters Description
Nice names Trainer calling the animals’ names in a motivating or exalted
way. Use of a high tone of voice.
Nice speeches
Trainer speaking to the canids in a motivating or exalted way,
usually when “gratifying” or “celebrating” a request
satisfactorily fulfilled. Use of a high tone of voice.
Neutral names
Trainer calling the animals’ names with a neutral intonation,
similar to that used to request cues during training. Use of words
without intense variations in intonation.
Neutral speeches
Trainer speaking to the canids with a neutral intonation, similar
to that used to request cues during training. Use of words
without intense variations in intonation.
Reprehensive names
Trainer calling the animals’ names with a high, reproachful tone
of voice, usually when inappropriate behaviors were exhibited by
the animals, such as dispersion, inattention, etc.
Reprehensive speeches
Trainer speaking to the canids with a high, reproachful tone o
f
voice, usually when inappropriate behaviors were exhibited by
the animals, such as dispersion, inattention, etc.
Laugh
Trainers’ laughter, produced usually in moments of relaxation,
when interacting in a playful manner with the animals or when
they did something funny.
Figure 1.
Illustrative spectrogram of a trainer’s speech. Blue boxes represent the marking of the
fundamental frequency (F0) of two distinct speech events. Arrows indicate the maximum (max.) and
minimum (min.) frequencies of these speeches. X axis: time interval (200 ms); y axis: frequency in
kHz.
Table 2.
Description of seven types of trainer vocalization, obtained for each training session with
socialized dogs (Canis familiaris) and wolves (Canis lupus).
Parameters Description
Nice names Trainer calling the animals’ names in a motivating or exalted way. Use of a high tone of voice.
Nice speeches
Trainer speaking to the canids in a motivating or exalted way, usually when “gratifying” or “celebrating” a
request satisfactorily fulfilled. Use of a high tone of voice.
Neutral names Trainer calling the animals’ names with a neutral intonation, similar to that used to request cues during
training. Use of words without intense variations in intonation.
Neutral speeches Trainer speaking to the canids with a neutral intonation, similar to that used to request cues during
training. Use of words without intense variations in intonation.
Reprehensive names Trainer calling the animals’ names with a high, reproachful tone of voice, usually when inappropriate
behaviors were exhibited by the animals, such as dispersion, inattention, etc.
Reprehensive
speeches
Trainer speaking to the canids with a high, reproachful tone of voice, usually when inappropriate
behaviors were exhibited by the animals, such as dispersion, inattention, etc.
Laugh
Trainers’ laughter, produced usually in moments of relaxation, when interacting in a playful manner with
the animals or when they did something funny.
All behavioral parameters considered here were coded from the videos by only one
person (author M.G.B.F.—Melissa Gabriela Bravo Fonseca) through Focal Sampling, with
Continuous Recording of behaviors, using the Solomon Coder program (Beta version
19.08.02, 2019, by András Péter). To obtain a measure of reliability of the behavior coding,
20% of all videos were re-coded (M.G.B.F.), and the scores compared with those of the
first viewing through a Spearman rank correlation; the results indicated a good agreement,
with correlation coefficients ranging from 0.86 to 0.99. The variables nice names, neutral
names, reprehensive names, correct responses and repetitions were evaluated in terms of
frequency (number of occurrences); all other variables were analyzed in terms of duration
(seconds).

Animals 2023,13, 1071 7 of 23
Table 3.
Description of the behaviors of socialized dogs (Canis familiaris) and wolves (Canis lupus),
obtained for each training session.
Parameters Description
Correct responses Mean number of cues correctly responded at first request.
Repetitions Mean number of repetitions of the cue when the animal did not respond to it at first request.
Latency Average intervals between cue request and the start of the expected response.
Visual orientation to trainer
Mean time the animal spent oriented towards the trainer (the orientation of its head deviating less
than 10◦from the trainer’s face).
Time at less than 1 m Mean time the animal spent within 1 m from the trainer.
Non-Training Behaviors
(NTBs)
Exploring: animal sniffs the ground or the walls.
Jumping: animal is either standing on its hind legs or jumping, with its four legs leaving the
ground, and touches the trainer with its forelegs.
Retreating: animal moves away from the trainer, abandoning the interaction.
Cortisol variations Difference between cortisol concentrations in the salivary samples collected before and after the
sessions.
Tail position/movements Retreated: tail pulled between the legs.
Wagging: tail moving from side to side.
In order to contribute to the understanding about the possible effects of different types
of speech on the animals’ responses, additional exploratory analyses were performed: a
comparison among acoustic parameters of the three types of speech, and an analysis of the
effects of these separate parameters on the animals’ responses.
2.6. Acoustic Comparison among Types of Speech
To investigate acoustic differences between the three types of speech classified in the
first stage of the study, we defined six acoustic variables (Table 4). Although all the speeches
present all the studied acoustic components (minimum frequency, maximum frequency,
peak frequency, average power), we separated the speeches into these components for
analysis.
Table 4.
Description of the acoustic variables used to characterize the trainers’ vocalizations extracted
from videos from training sessions with socialized dogs (Canis familiaris) and wolves (Canis lupus).
Parameters Description
Minimum frequency Measurement, in hertz (Hz), of the lowest frequency of the first harmonic of the speech.
Maximum frequency Measurement, in Hz, of the highest frequency of the first harmonic of the speech.
Average power Average value, in decibels (dB), of the power of the sound contained in the first harmonic of the speech.
Delta time
Duration, in seconds (s), of the speech, calculated by the difference between the end time and the begin time.
Peak frequency
Measurement, in Hz, of the frequency with the highest energy present in the first harmonic of the speech.
Number of speeches Sum of the number of speeches emitted by the trainers per session.
2.7. Animals’ Behaviors and Voice Acoustic Characteristics
We extracted audios from the videos recorded during all the 270 training sessions
with the Any Video Converter program, in WAV format. The recordings were analyzed
using the Raven Pro 1.5 software (Cornell Laboratory of Ornithology—Cornell University),
through the analysis of spectrograms.
The fundamental frequency (F0) of the first harmonic of each speech was selected in
the spectrogram (Figure 1), and the following parameters were considered for the analysis
of F0: (a) Visualization: spectrogram 1; (b) Channel: 1; (c) Brightness: 50%; (d) Contrast:

Animals 2023,13, 1071 8 of 23
50%; (e) Size of the spectrogram viewing window: 512; (f) Time interval (“x” axis): 200 ms;
and (g) Frequency in kHz (“y” axis): 0 to 2.80. Separate spectrograms for each of the three
types of speech analyzed in our study (nice, neutral, and reprehensive) can be seen in the
Supplementary Material (Figure S1). For this analysis, the category laugh was considered
as a positive non-verbal emotional vocalization [
25
], and therefore was included in the
category “nice”.
For each training session, the averages of the six acoustic variables used to analyze
the vocalizations emitted by the trainers (Table 4) constituted the explanatory variables for
this stage of the analysis. As response variables in these analyses, we used the same ones
described above: correct responses, latency, visual orientation to trainer, time at less than
1 m
, Non-Training Behaviors (NTB), and cortisol variation, in addition to the animals’ tail
position/movements (Table 3).
2.8. Statistical Analysis
The statistical analyses were performed by Generalized Linear Mixed Models (GLMMs),
with a Poisson distribution, adjusted for repeated measures using the R software. By adjust-
ing for repeated measures, we could use data from all sessions (3 sessions of each animal
with each of the 5 trainers), controlling for trainer and animal repeatability (random factor),
summing up 270 training sessions. We used “lme4” [
26
], “MASS” [
27
], “car” [
28
] and
“tidyverse” [
29
] packages to fit GLMM models in R statistical software, version 4.0.1. All
results were analyzed based on statistical significance (
α≤
0.05). We used the iterative
method (i.e., starting with the full model and removing explanatory variables with no effect
on the response variable). We built two models: GLMM1 to investigate the responses of
animals to the three types of speech, and GLMM2 to analyze the animal’s responses to
acoustic characteristics of the trainers’ vocalizations.
In the GLMM1 we evaluated the effects of the explanatory variables subspecies, nice
names and speeches, neutral names and speeches, reprehensive names and speeches, and
laugh on the response variables correct responses, repetitions, latency, visual orientation
to trainer, time at less than 1 m, NTBs, tail position/movements, and cortisol variations.
With the GLMM2, we evaluated the effects of subspecies, as well as the acoustic variables
of the trainers’ vocalizations (minimum frequency, maximum frequency, average power,
delta time, peak frequency, and number of speeches) on the response variables correct
responses, repetitions, latency, visual orientation to trainer, time at less than 1 m, NTBs, tail
position/movements, and cortisol variations.
In both GLMMs, the first model was always built including all data from dogs and
wolves, as well as possible two-way interactions between variables. Tables S1 and S2
present the full (initial) models of GLMM1 and GLMM2, respectively. If any explanatory
variable showed interaction with the variable subspecies, separate models were run for
each subspecies. Pearson correlations were run between the explanatory and the response
variables that did not allow the GLMMs to be run (e.g., due to data distribution).
For the acoustic characterization of the three types of speech (nice, neutral, and repre-
hensive), means and standard deviations of the variables (minimum frequency, maximum
frequency, average power, delta time, and peak frequency), in addition to the total number
of speeches, were calculated for each type of speech. Subsequently, repeated measures
ANOVA with Tukey’s post hoc (for normal distribution of data) or Friedman’s with Dunn’s
post hoc (for non-normal data) were run to check for differences among the types of speech,
considering each of the acoustic parameters of sound mentioned above.
3. Results
3.1. Animals’ Behaviors and the Types of Speech
Table S3 presents the GLMM models with the analyses of the animals’ responses to
the types of speech, with both subspecies together. Tables 5and 6present the variables
that had effects only for dogs and only for wolves, respectively. The response variables

Animals 2023,13, 1071 9 of 23
retreating and tail retreated, evaluated by Pearson correlations for the two subspecies, are
available in Table 7.
Table 5.
Final Generalized Linear Mixed Models * run for dogs (Canis familiaris), evaluating the
effects of the variables nice speeches, nice names, neutral speeches, neutral names, and reprehensive
speeches on the response variables correct responses, exploring, jumping, and tail wagging.
Response Variables Explanatory Variables Estimate ±sd z-Value p
Correct responses (intercept) 3.83 ±0.03 110.66 0.000
Reprehensive speeches −0.95 ±0.33 −2.80 0.005
Exploring (intercept) −0.91 ±0.38 −2.37 1.75
Nice names 0.28 ±0.04 7.50 <0.0001
Jumping (intercept) −1.31 ±0.42 −3.08 0.002
Neutral speeches 0.04 ±0.01 3.04 0.002
Tail wagging
(intercept) 4.21 ±0.34 12.51 <0.0001
Nice speeches 0.003 ±0.000 7.53 <0.0001
Neutral speeches 0.006 ±0.001 4.37 <0.0001
Reprehensive speeches −1.68 ±0.24 −7.00 <0.0001
Neutral names −
0.014
±
0.003
−4.13 <0.0001
* Explanatory variables without effect on the response variables were removed from the models during the model
selection process.
Table 6.
Final Generalized Linear Mixed Models * run for wolves (Canis lupus), evaluating the effects
of the variable nice speeches, nice names, neutral speeches, neutral names, reprehensive names, and
laugh on the response variables visual orientation to trainer, time at less than 1 m, exploring, and tail
wagging.
Response Variables Explanatory Variables Estimate ±sd z-Value p
Visual orientation to
trainer
(intercept) 5.64 ±0.33 16.90 <0.0001
Nice names −
0.021
±
0.004
−5.51 <0.0001
Neutral names −
0.016
±
0.001
−10.44 <0.0001
Reprehensive names −
0.034
±
0.009
−3.64 <0.0001
Time at less than 1 m
(intercept) 5.61 ±0.33 16.82 <0.0001
Nice speeches 0.002 ±0.000 5.07 <0.0001
Nice names −
0.018
±
0.004
−4.78 <0.0001
Neutral names −
0.011
±
0.001
−7.49 <0.0001
Reprehensive names −
0.019
±
0.009
−2.23 0.002
Exploring (intercept) 2.90 ±0.33 8.68 <0.0001
Nice names 0.08 ±0.01 7.37 <0.0001
Tail wagging
(intercept) 0.87 ±0.35 2.46 0.01
Nice speeches 0.011 ±0.002 4.26 <0.0001
Neutral speeches −
0.020
±
0.005
−4.12 <0.0001
Neutral names 0.026 ±0.01 2.71 0.007
Reprehensive names −0.57 ±0.14 −4.22 <0.0001
Laugh 0.05 ±0.02 2.91 0.003
* Explanatory variables without effect on the response variables were removed from the models during the model
selection process.

Animals 2023,13, 1071 10 of 23
Table 7.
Pearson correlations run for dogs (Canis familiaris) and wolves (Canis lupus), considering the
effects of the variables nice speeches, nice names, neutral speeches, neutral names, and reprehensive
names on the response variables retreating and tail retreated.
Subspecies Response Variables Explanatory Variables Pearson p-Value
Dog Retreating Nice speeches 0.23 0.01
Neutral speeches 0.18 0.04
Wolf Retreating Neutral names 0.30 0.28
Reprehensive names <0.001 <0.001
Dog Tail retreated Nice names 0.22 0.01
Wolf Tail retreated Neutral names 0.17 0.05
For both dogs and wolves, we identified positive correlations between exploration by
the animals and the emission of nice names by the trainer, and also between tail wagging
and nice speeches (Figure 2). Regarding tail wagging, while for dogs we identified positive
correlations between this variable and neutral speech, for wolves this association was
negative. We also recorded negative association between tail wagging and neutral names
for dogs, and positive association of tail wagging and neutral names for wolves.
Animals 2023, 13, x 11 of 24
Figure 2. Dispersion of the time that dogs (Canis familiaris) and wolves (Canis lupus) spent tail
wagging in Positive Reinforcement Training sessions, as a function of the duration of “nice
speeches” during the sessions. Y axis = tail wagging duration of dogs and wolves (s); X axis =
duration of nice speeches (s). Each dot represents an individual training session.
For dogs only, there were direct associations between (a) jumping and neutral
speeches, (b) retreating and nice and neutral speeches, and (c) tail retreated and nice
names. Negative associations were identified between (d) correct responses and
reprehensive speeches and e) tail wagging and reprehensive speeches.
For wolves only, we identified inverse relations between the orientation to trainer
and the names emitted in the three types of voice (nice, neutral, and reprehensive).
Negative correlations were also observed between the names in the three intonations and
the time the wolves spent at less 1 m from the trainer. Still regarding the proximity
between the dyad, nice speeches correlated positively with the time the wolves spent
within 1 m from the trainers (Figure 3). We also found negative correlations between tail
wagging and reprehensive names, and a positive correlation between tail wagging and
laughing. Positive correlations were also identified between retreating versus neutral and
reprehensive names, as well as between tail retreated and neutral names.
Figure 2.
Dispersion of the time that dogs (Canis familiaris) and wolves (Canis lupus) spent tail
wagging in Positive Reinforcement Training sessions, as a function of the duration of “nice speeches”
during the sessions. Y axis = tail wagging duration of dogs and wolves (s); X axis = duration of nice
speeches (s). Each dot represents an individual training session.
For dogs only, there were direct associations between (a) jumping and neutral speeches,
(b) retreating and nice and neutral speeches, and (c) tail retreated and nice names. Negative

Animals 2023,13, 1071 11 of 23
associations were identified between (d) correct responses and reprehensive speeches and
(e) tail wagging and reprehensive speeches.
For wolves only, we identified inverse relations between the orientation to trainer and
the names emitted in the three types of voice (nice, neutral, and reprehensive). Negative
correlations were also observed between the names in the three intonations and the time
the wolves spent at less 1 m from the trainer. Still regarding the proximity between
the dyad, nice speeches correlated positively with the time the wolves spent within 1 m
from the trainers (Figure 3). We also found negative correlations between tail wagging and
reprehensive names, and a positive correlation between tail wagging and laughing. Positive
correlations were also identified between retreating versus neutral and reprehensive names,
as well as between tail retreated and neutral names.
Animals 2023, 13, x 12 of 24
Figure 3. Dispersion of the time that wolves (Canis lupus) remained within 1 m from the trainers in
Positive Reinforcement Training sessions, as a function of the duration of “nice speeches” during
the sessions. Y axis = time at less 1 m (s); X axis = duration of nice speeches (s). Each dot represents
an individual training session.
3.2. Acoustic Comparison among the Types of Speech
The acoustic comparison among of the three types of speech (nice, neutral, and
reprehensive) demonstrated measurable differences in acoustic characteristics (minimum
frequency, maximum frequency, delta time, peak frequency, and number of speeches;
Table S4). Nice speech was used more often (10.391 times), being more energetic at higher
frequencies, and having higher intensity than the neutral speech, but lower intensity than
reprehensive speech. Compared to the other categories, the latter had the most extreme
values in terms of duration, intensity, sound amplitude, and frequencies. Although we
recorded just 47 reprehensive speeches in the 270 sessions, their duration was longer than
that of the other types of speech. The neutral speech presented intermediate
characteristics between the other two types of speech, being deeper and more energetic at
low frequencies. Figure S2 shows the average acoustic characteristics (minimum
frequency, maximum frequency, peak frequency, average power, delta time, and number
of speeches) of the three different types of speech (nice, neutral, and reprehensive) for
each of the five trainers involved in the project (T1–T5).
3.3. Animals’ Behaviors and Voice Acoustic Characteristics
Some of the acoustic characteristics of the trainers’ voices showed interaction with
subspecies when analyzed for dogs and wolves together, but the effect was not
significant in the analysis separated for dogs and wolves. Table S5 presents the full final
model for analysis of the animals’ responses to the acoustic characteristics of the trainers’
voices (dogs and wolves together); Tables 8 and 9 show the final statistical results of the
GLMMs for dogs and wolves, respectively. The response variables retreating and tail
retreated were evaluated by separate Pearson correlations for each subspecies (Table 10).
Figure 3.
Dispersion of the time that wolves (Canis lupus) remained within 1 m from the trainers in
Positive Reinforcement Training sessions, as a function of the duration of “nice speeches” during the
sessions. Y axis = time at less 1 m (s); X axis = duration of nice speeches (s). Each dot represents an
individual training session.
3.2. Acoustic Comparison among the Types of Speech
The acoustic comparison among of the three types of speech (nice, neutral, and
reprehensive) demonstrated measurable differences in acoustic characteristics (minimum
frequency, maximum frequency, delta time, peak frequency, and number of speeches;
Table S4
). Nice speech was used more often (10.391 times), being more energetic at higher
frequencies, and having higher intensity than the neutral speech, but lower intensity than
reprehensive speech. Compared to the other categories, the latter had the most extreme
values in terms of duration, intensity, sound amplitude, and frequencies. Although we
recorded just 47 reprehensive speeches in the 270 sessions, their duration was longer than
that of the other types of speech. The neutral speech presented intermediate characteristics
between the other two types of speech, being deeper and more energetic at low frequencies.
Figure S2 shows the average acoustic characteristics (minimum frequency, maximum
frequency, peak frequency, average power, delta time, and number of speeches) of the

Animals 2023,13, 1071 12 of 23
three different types of speech (nice, neutral, and reprehensive) for each of the five trainers
involved in the project (T1–T5).
3.3. Animals’ Behaviors and Voice Acoustic Characteristics
Some of the acoustic characteristics of the trainers’ voices showed interaction with
subspecies when analyzed for dogs and wolves together, but the effect was not significant
in the analysis separated for dogs and wolves. Table S5 presents the full final model for
analysis of the animals’ responses to the acoustic characteristics of the trainers’ voices (dogs
and wolves together); Tables 8and 9show the final statistical results of the GLMMs for
dogs and wolves, respectively. The response variables retreating and tail retreated were
evaluated by separate Pearson correlations for each subspecies (Table 10).
Table 8.
Final Generalized Linear Mixed Models * run for dogs (Canis familiaris), analyzing the
effects of the variables minimum frequency; maximum frequency; delta time; average power; peak
frequency on the response variables correct responses; jumping; and tail wagging.
Response Variables Explanatory Variables Estimate ±sd z-Value p
Correct responses (intercept) 3.61 ±0.07 12.65 0.00
Minimum frequency 0.002 ±0.000 3.78 <0.001
Jumping
(intercept) −5.17±0.99 −5.21 <0.001
Maximum frequency 0.005 ±0.001 3.32 <0.001
Delta time 3.13 ±0.98 3.18 0.001
Tail wagging
(intercept) 4.10 ±0.40 10.18 <0.001
Maximum frequency 0.003 ±0.000 7.67 <0.001
Average power −0.01 ±0.00 −2.94 0.003
Delta time 0.40 ±0.07 5.39 <0.001
Peak frequency −
0.003
±
0.000
−4.42 <0.001
* Explanatory variables without effect on the response variables were removed from the models during the model
selection process.
Table 9.
Final Generalized Linear Mixed Models * run for wolves (Canis lupus), analyzing the effects
of the variables minimum frequency; maximum frequency; delta time; average power; peak frequency
and number of speeches on the response variables correct responses; visual orientation to trainer;
time at less than 1 m; jumping; and tail wagging.
Response Variables Explanatory Variables Estimate ±sd z-Value p
Correct responses
(intercept) 3.17 ±0.25 12.65 <0.001
Minimum frequency −
0.004
±
0.001
−3.70 <0.001
Delta time −1.13 ±0.20 −5.50 <0.001
Peak frequency 0.005 ±0.001 4.52 <0.001
Number of speeches 0.002 ±0.001 2.27 0.023
Visual orientation to
trainer
(intercept) 6.48 ±0.35 18.33 <0.001
Minimum frequency −
0.003
±
0.000
−5.73 <0.001
Average power −0.01 ±0.00 −8.84 <0.001
Delta time −0.20 ±0.06 −3.52 <0.001
Peak frequency 0.002 ±0.000 4.43 <0.001
Time at less than 1 m
(intercept) 5.90 ±0.34 17.13 <0.001
Minimum frequency −
0.002
±
0.000
−3.78 <0.001
Maximum frequency −
0.002
±
0.000
−6.28 <0.001
Delta time −0.15 ±0.05 −2.81 0.005
Peak frequency 0.003 ±0.000 5.30 <0.001

Animals 2023,13, 1071 13 of 23
Table 9. Cont.
Response Variables Explanatory Variables Estimate ±sd z-Value p
Jumping
(intercept) 0.23 ±1.70 0.14 0.891
Maximum frequency −0.02 ±0.00 −6.52 <0.001
Average power 0.15 ±0.03 4.87 <0.001
Delta time −2.30 ±0.97 −2.38 0.017
Tail wagging
(intercept) 4.15 ±0.80 5.17 <0.001
Maximum frequency 0.030 ±0.003 8.22 <0.001
Average power −0.12 ±0.013 −8.88 <0.001
Delta time 3.30 ±0.41 8.09 <0.001
Peak frequency −0.03 ±0.00 −9.29 <0.001
* Explanatory variables without effect on the response variables were removed from the models during the model
selection process.
Table 10.
Pearson correlations run for dogs (Canis familiaris) and wolves (Canis lupus), considering
the effects of the acoustic characteristics of the trainers’ voices (average power, number of speeches,
and maximum frequency) on the responses of the animals to training (retreating and tail retreated).
Subspecies Response Variables Explanatory Variables Pearson p-Value
Dog Retreating Number of speeches 0.21 0.01
Wolf Retreating Average power 0.26 <0.001
Wolf Tail retreated Maximum frequency −0.17 0.05
For both dogs and wolves, we identified positive correlations of tail wagging with
maximum frequency and delta time, and an inverse correlation of this variable with average
power and peak frequency (Figure 4). In other words, the duration of tail wagging was
associated with higher-pitched, longer, and softer speeches (with greater intensity at lower
frequencies).
Correct responses, for dogs, were positively related to the measurementes of minimum
frequency (higher pitched voices). In this case, this means that in sessions in which the
speeches had higher pitch, more correct responses were obtained from the dogs. For wolves,
correct responses were inversely associated with minimum frequency and delta time, and
directly correlated with peak frequency and the number of speeches, i.e., more correct
responses when the session had more speeches, shorter, and in a low pitched tone.
Jumping correlated with maximum frequency and delta time in both subspecies: for
dogs jumps were positively associated with higher pitched and longer vocalizations, and,
for wolves, this associacion was negative. For wolves, we also identified a direct association
between the number of jumps and average power. This means there was more jumping
in sessions with more low pitched, intense, and short speeches. Retraction was directly
related to the number of speeches (more speeches) in sessions with dogs, and with the
mean average power in sessions with wolves (more retraction in the sessions with a greater
use of high pitch).
In sessions with wolves, we observed that (a) the visual orientation to trainer was
negatively related to minimum frequency, average power and delta time, and directly
correlated to peak frequency (greater use of low-pitched, short speeches); (b) the time
spent within one meter from the trainer was positively associated to peak frequency,
and negatively related to minimum frequency, maximum frequency, and delta time (low-
pitched, and short speeches); and (c) tail retracted was directly correlated with maximum
frequency (high-pitched speeches).

Animals 2023,13, 1071 14 of 23
Animals 2023, 13, x 14 of 24
Table 10. Pearson correlations run for dogs (Canis familiaris) and wolves (Canis lupus), considering
the effects of the acoustic characteristics of the trainers’ voices (average power, number of speeches,
and maximum frequency) on the responses of the animals to training (retreating and tail retreated).
Subspecies Response Variables Explanatory Variables Pearson p-Value
Dog Retreating Number of speeches 0.21 0.01
Wolf Retreating Average power 0.26 <0.001
Wolf Tail retreated Maximum frequency −0.17 0.05
For both dogs and wolves, we identified positive correlations of tail wagging with
maximum frequency and delta time, and an inverse correlation of this variable with
average power and peak frequency (Figure 4). In other words, the duration of tail
wagging was associated with higher-pitched, longer, and softer speeches (with greater
intensity at lower frequencies).
Figure 4. Dispersion of the time dogs (Canis familiaris) and wolves (Canis lupus) spent tail wagging
in Positive Reinforcement Training sessions, as a function of acoustic characteristics of the trainers’
voices. Y axis = tail wagging duration of dogs and wolves (s); X axis = (A) maximum frequency
(Hz), (B) average power (dB), (C) delta time (s), and (D) peak frequency (Hz). Each dot represents
an individual training session.
(A)
(B) (C)
(D)
Figure 4.
Dispersion of the time dogs (Canis familiaris) and wolves (Canis lupus) spent tail wagging
in Positive Reinforcement Training sessions, as a function of acoustic characteristics of the trainers’
voices. Y axis = tail wagging duration of dogs and wolves (s); X axis = (
A
) maximum frequency
(Hz), (
B
) average power (dB), (
C
) delta time (s), and (
D
) peak frequency (Hz). Each dot represents an
individual training session.
3.4. Cortisol
As described for Vasconcellos and colleagues [
18
], the cortisol concentrations in the
saliva samples taken after the training sessions were lower compared to the samples
taken before the sessions in both wolves and dogs (t =
−
2.864, p= 0.004). The mean
values for these concentrations were 1023.03
±
75.99 ng/mL (wolves before training);
820.13 ±64.03 ng/mL
(wolves after training); 2280.87
±
153.2 ng/mL (dogs before train-
ing); and 1851.99
±
162.9 ng/mL (dogs after training). However, none of the explanatory
variables investigated here had a measurable effect on salivary cortisol.
4. Discussion
This is a study on the evaluation of the responses of socialized wolves and dogs to
human speech during training sessions. Trainers used three distinct types of speech with
the animals: nice, neutral, and reprehensive, each with particular acoustic characteristics.
Specific features of the prosody and content of the trainers’ speeches correlated to different
behaviors from the animals. For both subspecies, the time a trainer used nice speech

Animals 2023,13, 1071 15 of 23
within a training session was positively correlated to tail wagging, while the duration of
reprehensive speech was negatively associated to it. For dogs, the time reprehensive speech
was used within a session also negatively related to correct responses. For wolves, retreat
occurred more often the more reprehensive speech was used, while they spent greater
time within one meter of the trainer in sessions the trainer used nice speech for longer. In
addition, dogs tended to respond more to high-pitched voices, while wolves responded
more often to low pitch.
4.1. Animals’ Behaviors and the Types of Speech
We recorded some similar responses from dogs and wolves to the duration/frequency
with which different types of speech were used during the training sessions. The animals
wagged their tails more often in sessions in which nice speech was used for longer. This
result suggests that friendly interactions between trainer and animal have the potential
to improve the emotional conditions of the animals, since tail wagging has already been
reported as an expression of a positive emotional disposition (e.g., [
30
–
33
]). The high-
pitched tone—one of the aspects that characterizes the nice speech—was also characteristic
of DDS-type speeches. DDS has been used to attract the listener ’s attention—increasing the
recipients’ social responsiveness—and to meet the receiver’s emotional needs, possibly en-
hancing affiliative communication with the speaker by stimulating affection and emotions
with a positive valence [13,34].
Consistent with the animals’ behaviors associated to the nice speech, for both sub-
species we recorded that the longer reprehensive speech was used the more negative
emotional indicators were observed (reduction in tail-wagging and the time animals spent
next to the trainer, and increase in retreat), and the poorer the animals’ performances (e.g.,
correct responses). Although our training procedures were based on positive reinforce-
ment, it is possible that both dogs and wolves perceived sporadic events of reproachful
speech as punishment [
35
,
36
]. This perception may have promoted effects on the internal
state of the animals [
33
,
36
] and a consequent decrease in the rate of correct responses of
dogs. Mills and colleagues [
37
] investigated whether cues with different inflections and
emotional content influenced the behavior of dogs. Their results showed that requests
pronounced with displeasure or anger were associated to less predictable responses from
the animals compared to cues pronounced in neutral or “happy” voice tone. Other evidence
indicates that dogs have their behavior influenced by phonetic changes that occur during
the emission of verbal cues [
38
], and that the performance of dogs in solving tasks can be
compromised by their emotional dependence on people [39].
For wolves, tail wagging occurred more often when trainers laughed in the training
sessions. In the human context, the communication of emotions such as happiness leads
each actor to become aware of the other’s euphoric feelings, forging a mutual emotion
that acts to cement human social relationships [
40
]. It is possible that—to some extent—
this phenomenon extends to an interspecific scenario. The sections in which moments
of relaxation of the trainers—expressed through laughs—were more frequent may have
influenced the wolves through contagion. Contagion has been considered adaptive be-
cause it allows animals to respond appropriately to different situations, a capacity that
favors survival, increases reproductive success [
41
,
42
], and has already been described
in wolves [
43
]. Feelings of joy may be communicated through positive facial cues (such
as smiling) and vocal cues (laughter) [
44
–
46
]. Non-verbal interjections—such as laugh-
ter, screams, and yelps—contain rich affective information, and can be perceived as the
auditory equivalent of facial emotional expressions, which most often accompany these
vocal manifestations [
47
]. Therefore, laughter might have provided a gentler environment,
contributing to the relaxation of the wolves [18,36].
We also recorded a positive relationship between the time the trainers used nice speech
and the time the wolves spent within one meter from the trainer, suggesting that the use of
such a vocal approach may have favored their interest in the training dynamics, bringing
the dyad closer together. An alternative interpretation of this association could be that the

Animals 2023,13, 1071 16 of 23
trainers used softer voices when the animals were attentive and closer to them. However,
as preference for DDS has already been demonstrated to influence dog proximity and
time spent looking at humans [
13
,
48
], we believe the alternative interpretation is unlikely.
Although this association has not been demonstrated for wolves so far, the reduction in the
time they spent close to the trainers when these used reproachful speech more often during
the training sessions is consistent with a greater interest in approaching and interacting
with someone using a DDS-type of speech [
18
,
34
]. In this study, nice speech was used
mainly when gratifying or celebrating a request satisfactorily fulfilled. One can interpret
the responses of the animals to the different types of speech as classically conditioned
responses (i.e., an association of the nice speech with the food reward). But, in view of the
already mentioned and discussed effects of the use of nice and reprehensive speeches, we
believe that interpreting the responses of dogs and wolves as purely conditioned behaviors
would bring only a limited facet of the situation. Besides, this rationale could not be used
to interpret the animals’ responses to reprehensive speech, since this type of speech was
not used in situations of incorrect responses to training (and therefore was not connected to
withholding a reward). In this sense, we believe that phylogeny and ontogeny interacted
in the development of the dogs’ characteristics, favoring interspecific communication such
as the type established during the dynamics of PRT, e.g., [6].
Although our analyses have been based on correlations between variables, which do
not assess causality, our results point to associations between the environment in a training
session (positive: nice speech; negative: reprehensive speech and behaviors of animals)
possibly interfering with the animals’ perception of the training context, compliance with
requests, attention, and emotional state. It seems that animals interpret cues not only
as simple discriminative stimuli, but also as a range of sound signals related to specific
contexts including novel physical and social stimuli [
49
]. This has an important practical
effect on the welfare and performance of animals, since variations in the training context
might “obscure” the meanings of commands instead of improving understanding, and
produce inconsistent feedbacks compared to what was expected [
37
]. That is, the animals
can be influenced by several factors related to verbal communication, such as a reprehensive
intonation, for example, leading to responses that are less consistent with the expectations
of a training model. These findings recommend the use of a friendly approach even if the
animals present responses that are different from the ones requested. This strategy has the
potential to create a positive atmosphere, which may favor compliance with commands,
and the development—by the animals—of a more positive emotional disposition [36].
Some of our results seem paradoxical at first sight. We found increased jumping and
retraction by dogs in sessions when the trainers used more often nice and neutral speeches.
This result can be interpreted as an aversive response from dogs to these kinds of speech.
This interpretation is possible, but not likely, given that the same types of speech were also
associated with a greater duration of tail wagging in dogs and in wolves. Considering
that these behaviors can compromise training performance, an alternative—and possibly
more sensible—interpretation would be that this result is a consequence of the trainers
trying to dissuade the dogs from jumping and retreating, by increasing the duration of the
neutral and nice speeches to calm them down, and direct their focus back to the requested
cues [
36
,
50
,
51
]. The frequency with which nice names were used in a training session
was positively associated to the exhibition of exploration by the animals—a non-training
behavior. This association also may have been a delicate and affectionate attempt by the
trainers to attract the attention of the animals during moments of dispersal, as observed in
dogs in a previous study [
36
]. The same rationale can be used also to interpret the negative
correlation between the frequency of trainers’ calling of wolves’ names in any of the three
different intonations (nice, neutral, or reprehensive) with animals’ orientation and time
spent within one meter from the trainer. These results suggest that in situations when
the wolves were unfocused or not interested in the interactions, the trainers might have
increased the frequency of calls to capture the animals’ attention again so that they would
return to training [36].

Animals 2023,13, 1071 17 of 23
4.2. Acoustic Comparison among Types of Speech
This analysis, although explorative in terms of other parameters than pitch, and run
to help raise hypotheses for future studies, identified consistent differences between the
average acoustic characteristics of the three types of speech recorded. Some of these results
corroborate a previous study [
52
], in which speech constituents with different intonations
were investigated. In that study, the “neutral speeches”, in addition to being shorter, had
little change in F0, and their outline was smooth and continuous. On the other hand, “angry
expressions” were longer and had a higher F0, with more abrupt intervals between one
harmonic and another, suggesting these were generated with more emphasis. In addition,
the study authors observed that the duration of neutral speeches was shorter than that of
angry speeches. Benjamin & Slocombe [
13
] also argue that DDS-style speeches, compared
to ADS-style, have a higher pitch and exaggerated affect, characteristics that were also
observed in this study. Therefore, nice speech in our study, to some extent, matched
DDS, not only considering its acoustic characteristics, but also regarding dogs’ behaviors
associated to it.
Although the nice and reprehensive speeches had some similar characteristics (high-
pitched and high-energy at elevated frequencies), we recorded differences between them in
other acoustic variables (minimum frequency, maximum frequency, and average power).
Different degrees of emotional activation influence tonal and energetic aspects of
sound [53,54]
.
Social stress—such as stress generated during rough interactions, or after unmet expectations—
can be responsible for disturbing neurophysiological, behavioral, and emotional patterns of
individuals [
55
]. The differences we found in the acoustic parameters of different speeches
may be related to their different valences: a “positive” emotion with physiological arousal
versus a “negative” emotion with physiological arousal, respectively [
54
]. As a result, an in-
crease in the individuals’ respiratory rate and in subglottic pressure during speech increases
the relative energy of the upper harmonics and the fundamental frequency. Fundamental
frequency (F0—the acoustic element analyzed in this project) is a sound component that
usually presents great variation, being indicated for speech analysis regarding the intention
and the internal state of the speaker. For example, at high psychophysiological arousal, F0
increases, as well as the amplitude, intensity, variability and rhythm of sound. On the other
hand, low excitation patterns are associated with reduced F0, narrow bands, low sound
energy, and small variability and slow cadence. In addition, modifications of the F0 height
and range have been associated with attentional and affective functions [
56
]. Increased
pitch range (great sound amplitude), as observed in this study in nice speech, is usually
related to gaining audience attention and is found in speeches addressed to audiences with
limited attention abilities, such as babies or pet dogs [
57
,
58
]. It is possible that the lack of
attention or objectivity of the animals in certain circumstances has resulted in sporadic
events of impatience from the trainers, and therefore in the emission of speeches with
different acoustic patterns.
Considering that human voice can be modulated and influenced by several factors
intrinsic to the sender or associated with the context, the naturalistic condition in which our
study was developed allowed us to unveil the influence of several factors on the animals’
responses to human speech. As the speakers, when modulating the voice, send verbal,
non-verbal, and even emotional linguistic information to communicate [
55
], if our study
had been developed based on a predetermined set of sentences and voice intonations, we
would possibly have failed to record important nuances associated to the trainers, to the
social situation, and to the preferences of the animals, for example [34].
4.3. Animals’ Behaviors and Voice Acoustic Characteristics
The acoustic characteristics of the trainers’ voices correlated to different responses
from dogs and wolves. In general, dog behavioral changes occurred more often within
a session when the trainers talked using high intonations, while most wolf responses
were correlated with lower intonations. The study of Ben-Aderet and colleagues [
11
], the
first to investigate the behavioral responses of dogs to DDS, found that puppies prefer

Animals 2023,13, 1071 18 of 23
DDS to ADS, while adult and old dogs showed no preference for any type of speech—an
outcome later extended to adult dogs [
13
]. Some of the most common pets (i.e., dogs and
cats) exhibit neotenous morphological and behavioral characteristics throughout life [
59
].
The retention of juvenile traits in adulthood (neoteny) is considered a by-product of the
domestication process [
60
–
62
]. Perhaps the tendency of dogs to respond more to high-
pitched speeches—a characteristic more intensively exhibited by pups—is associated with
neotenic patterns, in which there is a preference for more affectionate vocal interactions.
Furthermore, dogs’ preferences for heightened prosodic traits may have an evolutionary
explanation. Mammals are known to use acoustic signals to demonstrate motivations,
intentions, and emotional states in specific contexts [
63
,
64
]. For example, high tonal sounds
are associated with affiliative or submissive motivation probably because they imitate the
sounds produced by babies (leading to a calming effect over the receiver) [65].
An alternative explanation for the preference of dogs for high-pitched voices is a
possible selection of auditory capabilities. Human beings are a very vocal species; we
rely on this communicative channel to establish interactions with conspecifics and other
social partners, and this extends to pets. Humans not only talk to them, but also teach
them to respond to certain acoustic signals [
2
]. As demonstrated in other studies, some
characteristics of DDS (e.g., greater pitch, used in an exaggerated and affected manner, and
a slower rhythm compared to ADS) are associated with the establishment and maintenance
of social connections between sender and receiver. Dogs’ preference for DDS over ADS [
13
],
and the demonstration that the neural activation in face of positive valence vocalizations
in dogs is similar to the activation in humans [
1
] suggest dogs may have a particular
perception of human voice. Such a perception may have improved during domestication,
and therefore become more sophisticated compared to wolves.
It is also possible that dogs, due to their great dependence on humans [
66
], noticing
the trainers’ proneness to friendly interactions, showed pro-social behaviors, to strengthen
bonds with them [
13
]. Attachment between a dog and its human attachment figure creates
a secure relationship for both [
67
,
68
], and can interfere with behavioral patterns associated
with loyalty and the desire to please this human [
67
–
69
]. From an evolutionary perspective,
it is likely that dogs that responded to humans emitting high-pitched speeches might have
obtained greater fitness than those that did not respond to them. Therefore, this type of
interaction might have been essential to the animals during the first stages of domestication,
acting in the establishment of a strong bond with humans and thus increasing the chance
of the animals obtaining better resources. Hasting [
70
] explored how human communica-
tive behavior may have influenced the evolutionary process of domestication of dogs by
analyzing the use of motherese speech, a DDS-equivalent, also often used by humans
when interacting with their dogs. Observing the effectiveness of motherese in human
encounters with socialized wolves, the author speculated that dog domestication may have
been influenced by human auditory communication patterns. She compared the behavioral
reactions of captive wolves, dogs, and hybrids of these two subspecies by using separate
auditory stimulus patterns: motherese, normal speeches, and mute (no vocal stimuli), and
recorded a strong preference of dogs for motherese, while wolves and wolf-dog hybrids
showed no preference for any auditory stimulus over another. Our outcomes corroborate
Hasting’s work [
70
] in regard to dogs’ reactions to high-pitched, intense speech, and points
to a different pattern in wolves—a greater interest in low-pitched speeches—likely due to
the lack in this subspecies of the domestication effects [
71
]. These results may be taken as a
standpoint for future studies.
4.4. Study Limitations
One limitation of this study is that we did not analyze the direct responses of animals
towards human speech, but rather how the duration/frequency of the type of speech within
5 min training sessions might have influenced the animals’ behaviors. Differently from
other studies e.g., [
11
,
13
], we can draw no conclusions on how a certain type of speech
directly influences the behavior of the animals. Thus, we only draw conclusions of how

Animals 2023,13, 1071 19 of 23
the general atmosphere in the training sessions (e.g., the duration of the different types of
speech) might have influenced the animal’s reactions.
The responses of the animals in training sessions with more reproachful speech in-
dicated possible impacts of this type of discourse. However, reprehensive speeches were
much less frequently used (47 times in 270 sessions) than nice (10.281 times) and neu-
tral (7.500 times) speeches, limiting conclusions. It is possible that characteristics of the
trainer, such as preference for certain individuals/subspecies have modulated their use
of speech. This type of limitation—common in naturalistic research—can be overcome
in future (non-naturalistic) studies by standardizing/equalizing the use of the different
speeches. The possible confounding influence of learned responses in the reactions of
the animals to training may be minimized by including in the sample naïve animals—i.e.,
animals not used to training, or by evaluating the animals’ responses to human speech
out of the training context. Our sample, although among the average sizes of studies on
socialized wolves, e.g., [
72
–
74
], is not large compared to studies on domesticated species. In
order to deal with this constraint, we adjusted our analysis for repeated measures, therefore
making it possible to include data from all 270 sessions in the analysis. Finally, it is known
that communication usually occurs in a multimodal context. For example, studies have
indicated an intermodal ability in dogs related to the integration of visual and auditory
emotional cues [
75
,
76
]. The focus of our study has been on the association between dogs’
and wolves’ behavior and human speech, not investigating other communicative channels.
Future studies should integrate analysis on responses to vocalization with other factors
involved in communication, such as gestures, gaze direction, posture, and facial expres-
sions. Such studies have the potential to still improve our understanding of interspecific
communication during training.
5. Conclusions
The trainers in our study used three distinct types of speech with the animals: nice,
neutral, and reprehensive, each with distinct acoustic characteristics. Nice speech was
associated with the exhibition by the animals of behaviors indicative of confidence, agree-
ableness, positive arousal, and proximity between trainer and animal. Reprehensive speech
had an opposite effect, having been associated with a decrease in tail wagging, in the rate
of correct responses, and with increased retreat. These results reiterate the importance of
considering vocal intonation as an important factor in interactions with canids (and possi-
bly other species). Hence, we identified some factors that have the potential for optimizing
training conditions, considering both animal performance and the establishment of pleasant
interactions between them and their human partners. In addition, our results indicated
that most dog responses were associated with high intonations, while wolf responses were
correlated with low intonations, a phenomenon possibly associated to the domestication
process, which may have favored in dogs the exhibition of neotenic patterns.
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/ani13061071/s1, Figure S1: Illustrative spectrogram of trainer’s
speeches. Red boxes represent the marking of the fundamental frequency (F0) of distinct speech
events in the three categories studied: nice (A), neuter (B) and reprehensive (C). Arrows indicate
the maximum (max.) and minimum (min.) frequencies of these speeches. X axis: time interval
of 200 ms; y axis: frequency in kHz; Table S1. Full (initial) Generalized Linear Mixed Models
run for dogs (Canis familiaris) and wolves (Canis lupus), considering the effects of the explanatory
variables (subspecies; nice names—NiN; neutral names—NeN; reprehensive names—ReN; nice
speeches—NiS; neutral speeches—NeS; reprehensive speeches—ReS; and laugh—L) on the response
variables (visual orientation to trainer—vot; exploring—exp; jumping—jump; time at less than
1 m
—less; correct responses—correct; and tail wagging—waggp); Table S2. Full (initial) Generalized
Linear Mixed Models run for dogs (Canis familiaris) and wolves (Canis lupus), considering the effects
of the explanatory variables (subspecies; minimum frequency (Min_Freq); maximum frequency
(Max_Freq); average power (Avg_Pow); delta time (delta_time); peak frequency (Peak_Freq); and
number of speeches (num_speeches) on the response variables (visual orientation to trainer—vot;

Animals 2023,13, 1071 20 of 23
exploring—exp; jumping—jump; time at less than 1 m—less; correct responses—correct; and tail
wagging—waggp); Table S3. Final Generalized Linear Mixed Models run for dogs (Canis familiaris)
and wolves (Canis lupus), considering the effects of the explanatory variables (subspecies; nice names—
NiN; neutral names—NeN; reprehensive names—ReN; nice speeches—NiS; neutral speeches—NeS;
reprehensive speeches—ReS; and laugh—L) on the response variables (visual orientation to trainer;
exploring; jumping; time at less than 1 m; correct responses; and tail wagging); Table S4. Results of
Dunn and Tukey post hoc tests comparing the acoustic parameters (minimum frequency, maximum
frequency, delta time, peak frequency, number of speeches, average power) of nice, neutral and
reprehensive speeches emitted by trainers in Positive Reinforcement Training sessions with dogs
(Canis familiaris) and wolves (Canis lupus); Table S5. Final Generalized Linear Mixed Models run for
dogs (Canis familiaris) and wolves (Canis lupus), considering the effects of the explanatory variables
(subspecies; minimum frequency—MinF; maximum frequency—MaxF; peak frequency—PF; average
power—AP; delta time—DT; and number of speeches—NS) on the response variables (visual ori-
entation to trainer; exploring; jumping; time at less than 1 m; correct responses; and tail wagging);
Figure S2. Characterization of voice of the five trainers involved in Positive Reinforcement Training
sessions with dogs (Canis familiaris) and wolves (Canis lupus). Left to right, bottom margin: trainers
1, 2, 3, 4, and 5. Acoustic parameters of trainers’ voices: minimum frequency, maximum frequency,
peak frequency, average power, delta time, and number of speeches. Number of nice, neutral, and
reprehensive speeches: 10.391, 7.500, and 47, respectively.
Author Contributions:
Conceptualization: A.d.S.V., K.K., F.R., Z.V. and M.G.B.F.; methodology:
A.d.S.V., K.K., F.R., Z.V., M.G.B.F. and M.H.L.D.; formal analysis: M.G.B.F., H.O.H., A.d.S.V., M.H.L.D.
and L.C.G.P.; investigation: A.d.S.V., K.K., F.R., Z.V., M.G.B.F. and H.O.H.; data curation: M.G.B.F.,
H.O.H. and A.d.S.V.; writing—original draft preparation: M.G.B.F., A.d.S.V. and H.O.H.; writing—
review and editing: M.G.B.F., A.d.S.V., H.O.H., K.K., F.R., Z.V. and M.H.L.D.; supervision: A.d.S.V.
All authors have read and agreed to the published version of the manuscript.
Funding:
M.G.B.F. received a social scholarship from the Pontifical Catholic University of Minas
Gerais. F.R. received funding from Austrian Science Funds (Project number: P34675-G), and A.d.S.V.
received funding from the National Council for Scientific and Technological Development (Process
number 309124/2022-0).
Institutional Review Board Statement:
All study animals were kept at the Wolf Science Center
(www.wolfscience.at: license n
◦
: AT00012014). The CITES (www.cites.org) import permits for the
animals are: 2008: Zoo Herberstein, Austria: AT08-B-0998, AT08-B-0996, AT08-B-0997; 2009: Triple D
Farm, USA: AT09-E-0018; 2012: Minnesota Wildlife Connection, USA: 12AT330200INEGCJ93. The
animals were housed in accordance with the Austrian Federal Act on the Protection of Animals
(Animal Protection Act—TSchG, BGBl. I Nr. 118/2004). Hence, in accordance with the Austrian
Animal Experiments Act (BGBl. I Nr. 114/2012, Tierversuchsgesetz 2012—TVG 2012) no ethical
approval was officially required, but we still obtained one from the University of São Paulo, Brazil
(Committee of Ethics for Animal Research from the Institute of Psychology, University of São Paulo,
Brazil, approval number 016.2009).
Informed Consent Statement: Not applicable.
Data Availability Statement:
Data available on request due to restrictions (e.g., privacy or ethical).
The data presented in this study are available on request from the corresponding author. The data are
not publicly available due to privacy restrictions.
Acknowledgments:
This study was supported by a scholarship from the Pontifical Catholic Univer-
sity of Minas Gerais, Brazil (MBF). We also thank the Foundation for Research Support of the State of
Minas Gerais (FAPEMIG) and the National Council for Scientific and Technological Development
(CNPq), for their continuous support for our research.
Conflicts of Interest: The authors declare no conflict of interest.
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