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Archives of Acoustics Vol. 49, No. 1, pp. 3–9(2024), doi: 10.24425/aoa.2023.146825
Research Paper
Enhancing Speech Recognition in Adverse Listening Environments:
The Impact of Brief Musical Training on Older Adults
Akhila R. NANDAKUMAR, Haralakatta Shivananjappa SOMASHEKARA ,
Vibha KANAGOKAR , Arivudai Nambi PITCHAIMUTHU∗
Department of Audiology and Speech-Language Pathology, Kasturba Medical College, Mangalore
Manipal Academy of Higher Education
Manipal, Karnataka, India
∗Corresponding Author e-mail: arivudainambi11@gmail.com
(received December 14, 2022; accepted July 23, 2023; published online December 11, 2023)
The present research investigated the effects of short-term musical training on speech recognition in adverse
listening conditions in older adults. A total of 30 Kannada-speaking participants with no history of gross
otologic, neurologic, or cognitive problems were divided equally into experimental (M=63 years) and control
groups (M=65 years). Baseline and follow-up assessments for speech in noise (SNR50) and reverberation
was carried out for both groups. The participants in the experimental group were subjected to Carnatic
classical music training, which lasted for seven days. The Bayesian likelihood estimates revealed no difference
in SNR50 and speech recognition scores in reverberation between baseline and followed-up assessment for the
control group. Whereas, in the experimental group, the SNR50 reduced, and speech recognition scores improved
following musical training, suggesting the positive impact of music training. The improved performance on
speech recognition suggests that short-term musical training using Carnatic music can be used as a potential
tool to improve speech recognition abilities in adverse listening conditions in older adults.
Keywords: musical training; carnatic music; speech recognition in noise; speech recognition in reverberation.
Copyright ©2024 The Author(s).
This work is licensed under the Creative Commons Attribution 4.0 International CC BY 4.0
(https://creativecommons.org/licenses/by/4.0/).
1. Introduction
Several anatomical and physiological changes oc-
cur in the auditory system of older adults as part
of the aging process (Chisolm et al., 2003). Aging
causes alterations in the metabolic activity of the
cochlea, leading to a decrease in endo-cochlear po-
tentials (EP) (Wangemann, 2002). This reduction
in EP impairs the functioning of the cochlear ampli-
fier and raises the neural threshold (Schmiedt et al.,
2002). Additionally, the aging process disrupts the
precise timing of the neuronal firing, resulting in in-
accuracies in the phase locking of auditory neurons
(Moser et al., 2006). Animal models of aging have
demonstrated a decrease in the size of spiral ganglion
cells in Rosenthal’s canal and a reduction of approx-
imately 15 to 25% of cells throughout the cochlear
duct (Mills et al., 2006). The progressive degenera-
tion of cells within the auditory system leads to var-
ious auditory perceptual deficits (Tun et al., 2012),
including reduced audibility (Schuknecht, Gacek,
1993), deterioration in suprathreshold auditory spec-
tral processing (Nambi et al., 2016), temporal pro-
cessing (He et al., 2008) and cognitive abilities (Ver-
haeghen, Cerella, 2002). These deficits can con-
tribute to difficulties in speech perception in noisy and
reverberant environments (Helfer, Wilber, 1990;
Nambi et al., 2016). The most common complaint
among older adults is the difficulty in comprehending
speech in adverse listening conditions. This difficul-
ty stems from their diminished auditory processing
abilities, which hinder their ability to separate tar-
get speech from background noise, resulting in reduced
speech perception in noisy environments (Schoof,
Rosen, 2014). Due to the decline in auditory process-
ing abilities in older adults, their passive and effort-
less speech processing in noisy environments is com-
promised (Rabbitt, 1990). As a compensatory mech-
4Archives of Acoustics – Volume 49, Number 1, 2024
anism, older adults rely on active and conscious signal
processing, which relies on intact cognitive function-
ing. However, the aging process also impacts cognitive
abilities, which can contribute to difficulties in speech
perception in noisy situations (Tun et al., 2002).
Methods to overcome communication difficulties in
older adults have been the topic of interest among
researchers. One preventive measure often recom-
mended to counter the effects of aging is engaging
in physical exercise (Alessio et al., 2002;Curhan
et al., 2013). Physical exercise may help in prevent-
ing age-related auditory disorders, although it re-
mains unclear whether it can reverse hearing changes
that have already occurred due to aging. Other stud-
ies have demonstrated the benefits of auditory training
using different stimuli, such as monosyllables (Burk
et al., 2006) and consonant-vowel transitions at the syl-
lable, word, sentence, and context levels (Anderson
et al., 2013). These training methods have improved
neural timing, processing speed, and speech perception
in noisy environments.
In a broader sense, musical training can be consid-
ered a form of auditory training, and long-term musical
training has been found to have positive effects on au-
ditory and cognitive abilities (Parbery-Clark et al.,
2009a;2009b;2012;2013;Kraus, Chandrasekaran,
2010;Patel, 2011). Older adult musicians with long-
term expertise retain neuro-physiological advantages
due to music which may improve their speech cod-
ing abilities. Anderson and Kraus (2010) found
that these musicians outperformed their non-musician
counterparts in tasks involving auditory, spectral, tem-
poral, and cognitive processing. These promising find-
ings suggest that musical training could serve as
an effective strategy to mitigate speech perception
deficits in older adults (Kraus, White-Schwoch,
2014). Therefore, it would be interesting to investigate
whether musical training can be employed as an audi-
tory training method to overcome speech recognition
deficits in challenging listening conditions.
To the best of our knowledge, only Jain et al.
(2015) investigated the effect of short-term musical
training on speech recognition in noise among young
adults, reporting enhancements in speech recogni-
tion. However, the impact of short-term musical train-
ing on speech recognition abilities in older adults re-
mains unexplored. Hence, the present study aims to
investigate the effects of short-term musical training
on speech recognition in adverse listening conditions
in older adults.
2. Method
The Institutional Ethics Committee (IEC) at Kas-
turba Medical College (KMC), Mangaluru, approved
the research protocol. A total of 30 participants were
selected using the convenient sampling method and
were evenly divided into experimental and control
groups. All participants were native Kannada speakers
with no prior musical training experience or significant
ear, neurological, or cognitive issues. Before conduct-
ing the study, informed consent was obtained from all
individuals. Table 1 depicts the mean age of the groups
with their average pure tone thresholds at 500 Hz,
1 kHz, and 2 kHz (PTA1), as well as 1, 2, and 4 kHz
(PTA2). An independent t-test revealed no statistically
significant difference (p>0.05) in PTA1 (t28 =1.619,
p=0.117) and PTA2 (t28 =1.337,p=0.192) between
the two groups.
Table 1. Age and hearing thresholds of all the participants
in the experimental and control group.
PTA (M & SD)
Mean age PTA1 PTA2
Experimental group 63 years 31.77 (6.50) 35.55 (6.53)
Control group 65 years 36.66 (9.71) 40.55 (12.92)
PTA1: average of pure tone thresholds at 500 Hz, 1 kHz, 2 kHz.
PTA2: average of pure tone thresholds at 1, 2, 4 kHz.
3. Procedure
The research was conducted in three distinct
phases. During the initial phase, participants from
both groups underwent testing to evaluate their speech
recognition ability in noisy and reverberant conditions.
In the subsequent phase, participants in the experi-
mental groups received music training. Finally, in the
last phase, the speech recognition ability in noise and
reverberation was reassessed for all participants in
both groups. Stimuli for speech recognition tests were
presented from a personal laptop and routed through
the Creative Soundblaster X-Fi USB sound card, while
the Sennheiser HD 280 Pro headphones were used for
stimulus presentation. All stimuli were digitized at
a sampling rate of 44 100 Hz. The signal processing
for speech recognition in noise and the music training
paradigm was implemented in the MATLAB version
7.10.0 platform. Additionally, the signal processing for
speech recognition in reverberation was performed us-
ing Adobe Audition Version 3 software.
4. Assessment of speech recognition in noise
The standard QuickSIN protocol was employed to
estimate speech recognition in noise. Two lists of the
standard QuickSIN Kannada (Methi et al., 2009)
sentences, spoken by the female speakers were used
as the targets and a 4-talker speech babble was used as
the background noise. Each list consisted of seven sen-
tences, with the first sentence presented at a signal-to-
noise ratio (SNR) of 20 dB. Subsequently, the SNR was
gradually decreased in 5 dB increments until reach-
ing −10 dB SNR for the final sentence. The sentences
A.R. Nandakumar et al. – Enhancing Speech Recognition in Adverse Listening Environments.. . 5
were presented at the most comfortable level (MCL)
of the participant. For each sentence, the count of cor-
rectly identified keywords by each participant was de-
termined and converted into the proportion of correct
responses for each list. The SNR required to achieve
a 50% correct recognition score (SNR50) was then es-
timated by fitting the cumulative Gaussian psychome-
tric function to the proportion of correct responses at
each SNR level. SNR50 was calculated as the mid-
point of the psychometric function, separately for each
list, and then averaged. In total, four sentence lists
were employed to assess SNR50, with two sets used for
pre-training evaluation and the remaining two sets
for post-training assessment.
4.1. Assessment of speech recognition in reverberation
A single list of sentences from the QuickSIN test
was convolved with binaural room impulse responses
(BRIRs) to simulate speech recognition in a reverber-
ant environment. This BRIR was generated to simulate
a standard rectangular auditorium with an average re-
verberation time of 0.6 seconds. The reverberant ma-
terial was presented to the participants at the MCL set
by the participants. The total count of accurately iden-
tified keywords was tallied, with a maximum achiev-
able score of 35. For assessment purposes, two sets of
sentences were utilized, one for the pre-training evalu-
ation and another for the post-training assessment.
4.2. Music training
The participants in the experimental group were
subjected to short-term musical training spanning ap-
proximately seven days. The training initially con-
sisted of ten Sampoorna ragas of Carnatic classical
music. The ascending and descending pattern (Aro-
hana and Avarohana) of all ten Sampoorna ragas were
recorded using violin, veena, and flute instruments
played by three professional artists with over ten years
of experience. These ten ragas were divided into two
lists, each containing five ragas. Subsequently, based
on a pilot study, only one list comprising the ra-
gas Mayamalavagowla, Kalyani, Thodi, Natabhairavi,
and Charukeshi was selected for the musical train-
ing. A custom training module was developed in the
graphical user interface (GUI) format, incorporating
a training component and an assessment module for
raga identification.
During the initial training session, the participants
were familiarized with all five ragas by listening to vi-
olin samples. The unique characteristics of each raga
were explained to them. Gradually, they were taught
to identify and discriminate the ragas based on the as-
cends and descends. Throughout the training, multiple
rehearsals and feedback were provided. At the end of
each session, the participant’s ability to identify each
raga was assessed by randomly presenting each raga
ten times. The training session continued until the par-
ticipant achieved a 100% correct score.
Once the training with the violin samples was com-
pleted, a similar process was followed using veena sam-
ples. In the final phase of the training, the participant’s
ability to transfer the knowledge of ragas acquired from
the violin and veena to the flute was ensured. In this
stage, the participants underwent a raga identification
test where each raga played on the flute was randomly
presented ten times. The training was considered fin-
ished when the participants achieved a flawless score
of 100%. If any participants failed to attain a per-
fect score of 100%, they were taken back to the pre-
vious stage, where they received further training with
veena samples. Once they achieved a perfect score for
the veena samples, they progressed to the next stage
for the raga identification test with flute samples. This
process continued until all participants obtained per-
fect scores of 100% for the flute samples.
5. Results
The statistical analyses were performed using the
JASP version 1.17.1.0 software. JASP is a compre-
hensive and user-friendly statistical software that of-
fers a wide range of tools for data analysis, includ-
ing Bayesian and frequentist methods. With its in-
tuitive interface and extensive statistical capabilities,
JASP provides researchers with a powerful platform for
conducting rigorous and transparent statistical ana-
lyses. In the current study, series of Bayesian paired
sample t-tests were employed to investigate the main
effect of music training on speech recognition outcomes
in noise and reverberation. Series of Bayesian inde-
pendent sample t-tests were performed to examine the
disparity in speech recognition performance in noise
and reverberation between the control and experimen-
tal groups.
5.1. Speech recognition in noise
The statistical analysis revealed that the SNR50 of
participants in the experimental group was signifi-
cantly different in the post-training session compared
to the pre-training session (BF10 =9.20). Music train-
ing had a positive influence by reducing the SNR50 in
the experimental group. On the other hand, there was
no significant difference in SNR50 between the base-
line and follow-up sessions (BF10 =0.44) in the control
group.
The statistical analysis revealed that there was no
significant difference in SNR50 between the control
group and experimental group in the pre-training ses-
sion (BF10 =0.35). However, after subjecting the ex-
perimental group to musical training, the SNR50 was
estimated in both the control and experimental groups.
6Archives of Acoustics – Volume 49, Number 1, 2024
The SNR50 in the experimental group was found
to be better than the control group (BF10 =141.5).
The mean and standard deviation of SNR50 in base-
line and follow-up sessions in both the control and ex-
perimental group is depicted in Fig. 1.
SNR50 [dB]
Pre-test Post-test
Control
Experiment
Fig. 1. Mean and standard deviation of SNR50 in baseline
and follow-up sessions for both the control and experimen-
tal groups.
5.2. Speech recognition in reverberation
The main effect of music training on speech recog-
nition scores in reverberation was evaluated by com-
paring the scores obtained in pre-training and post-
training sessions. The total correct speech recognition
scores of the participants in the experimental group
were significantly larger in post-training sessions than
in pre-training sessions (BF10 =7.57). This result sug-
gests that music training has improved speech recogni-
tion ability in reverberation. In contrast, there was no
significant difference (BF10 =0.45) in the baseline and
follow-up performance of the control group on speech
recognition scores.
Speech recognition
scores measured at the pre-train-
ing session were not different between the control and
experimental group (BF10 =0.26). The speech recog-
nition scores in reverberation measured following the
music training in the experimental group were higher
(BF10 =11.68) than the speech recognition scores of
the control group. The mean and standard deviations
of the correct scores are depicted in Fig. 2.
Speech recognition scores
Pre-test Post-test
Control
Experiment
Fig. 2. Mean and standard deviations of speech recognition
scores in baseline and follow-up sessions for both the control
and experimental group.
6. Discussion
The present study suggests a positive impact of
short-term musical training on speech recognition abil-
ities in older adults in adverse listening conditions.
This is evident from the improved SNR50 and speech
recognition scores under reverberant conditions. Pre-
vious research has consistently shown that musicians
tend to exhibit enhanced auditory abilities compared
to non-musicians, as demonstrated in various studies
(Kraus, Chandrasekaran, 2010;Kraus, White-
Schwoch, 2014;Musacchia et al., 2007;Parbery-
Clark et al., 2012;Rammsayer, Altenmüller,
2006;Slater et al., 2015;Strait, Kraus, 2011). Fur-
thermore, even older adults with musical experience
performed better than their non-musician counter-
parts in speech-in-noise tasks (Anderson et al., 2013;
Kraus, White-Schwoch, 2014;White-Schwoch
et al., 2013).
Electrophysiological studies have indicated that
long-term musical training can influence neural en-
coding by altering the responsiveness of sub-cortical
and cortical neurons, thereby enhancing auditory pro-
cessing ability. Recent electrophysiological studies fo-
cussing on short-term musical training lasting eight
days, conducted on young non-musicians, observed
changes primarily in cortical responses rather than
subcortical responses (Devi et al., 2015;Jain et al.,
2014). These findings provide evidence that even brief
musical training can lead to improvements in the neu-
ral encoding process. Thus, it can be inferred that the
improvements observed in the current study may also
be attributed to enhanced neural encoding mechanisms
associated with musical training.
Parbery-Clark et al.(2009) investigated subcor-
tical speech coding in musicians and non-musicians us-
ing speech-evoked auditory brainstem responses. They
found that the peaks of the waveform, carrying cru-
cial temporal cues, were better preserved in musicians
than in non-musicians, both in quiet and in the pres-
ence of background noise. Musicians also exhibited
enhanced phase-locking abilities compared to non-
musicians. The process of learning music enables the
auditory system to adapt and extract essential cues
from complex signals, resulting in improved neural rep-
resentation within the auditory system. It permits bet-
ter coding of the temporal and spectral aspects of the
signal and also helps in concurrent stream segregation,
which is essential for perceiving speech in adverse lis-
tening conditions (Zendel, Alain, 2009).
Exposure to music can strengthen the neural re-
sponses to stimuli and facilitate bottom-up processing.
The auditory efferent system, known for suppressing ir-
relevant background noise, can enhance the perception
of target speech (Luo et al., 2008;Zhang et al., 1997).
Through prolonged musical training, top-down pro-
cessing may modulate neural responses and magnify
A.R. Nandakumar et al. – Enhancing Speech Recognition in Adverse Listening Environments.. . 7
the cues that are important for stimulus identifica-
tion. The formation of the auditory template plays
a vital role in speech perception, and speaker iden-
tification (Best et al., 2008). It is possible when good
timber perception produces an excellent harmonic rep-
resentation of the complex stimulus. A good percep-
tion of timbre, which generates a high-quality har-
monic representation of complex stimuli, was observed
in musicians compared to non-musicians. Additionally,
musicians exhibited heightened sensitivity to subtle
harmonic changes (Musacchia et al., 2008;Zendel,
Alain, 2009). These factors could have also influenced
our study, potentially contributing to improved speech
performance in older adults following musical training.
Various hypotheses have been proposed to ex-
plain the musical training-dependent changes in audi-
tory processing abilities. Patel (2011) introduced the
OPERA (overlap, precision, emotions, repetition, and
attention) hypothesis, which offers potential explana-
tions for the changes observed in auditory processing
abilities resulting from musical training. The overlap
hypothesis suggests anatomical overlap in the brain
networks responsible for processing music and speech.
According to the precision theory, the heightened pre-
cision required for music processing can also be ben-
eficial for speech processing. The emotion theory pro-
poses that the positive emotions evoked by music acti-
vate the brain’s reward centres, leading to neural plas-
ticity. Additionally, the brain’s networks are frequently
exposed to musical stimuli, leading to the repetition
effect. Lastly, the attention theory states that the net-
works engaged in music processing are linked to fo-
cused attention, which is also crucial for recognizing
speech in noisy environments. Therefore, the OPERA
hypothesis provides a framework for understanding the
improvements in auditory processing and speech recog-
nition abilities in challenging listening conditions asso-
ciated with music training.
Musical training also presents challenges to short-
term memory and attention. Throughout the train-
ing, the participants were required to listen attentively
to the ragas being played, placing a cognitive load
on their memory as they aimed to recognize the raga
based on the notes. Patel (2011) hypothesized that
focused attention on the intricate details of the mu-
sical sounds promotes plasticity. Studies on animals
have also demonstrated that training-induced plastic-
ity is enhanced when active listening is involved (Fritz
et al., 2005). Anderson et al.(2013) expressed a simi-
lar viewpoint, emphasizing the importance of cognitive
involvement in auditory training programs. The cogni-
tive demand on memory leads to an increased reliance
on perceptual cues mediated by the prefrontal cortex.
Consequently, the perceptual demands and memory
interacted during the training program to strengthen
the neural representation of speech perception in the
presence of background noise. Therefore, the height-
ened cognitive load experienced during the training is
likely to positively impact the auditory processing and
speech recognition abilities of older adults.
Kraus and White-Schwoch (2014) believed that
music training enhances auditory processing regardless
of duration and intensity. The findings of the present
study align with their viewpoint, indicating that short-
term musical training improves auditory processing
and speech recognition abilities in older adults. Con-
sequently, short-term music training holds potential
as a way to alleviate auditory processing and speech
recognition deficits in older adults. However, further
investigation is necessary to determine the minimum
duration of training required to maintain the general-
ized benefits. This aspect presents a promising avenue
for future research in this field.
One notable finding in the present study is the ex-
tent of improvement observed in SNR50. Specifically,
the magnitude of improvement observed in our study
slightly exceeds that observed in long-term trained mu-
sicians who are native English speakers (Parbery-
Clark et al., 2009b). Conversely, Jain et al.(2015)
reported a similar magnitude of improvement follow-
ing short-term music training in young native Kan-
nada language speakers. These observations lead to the
speculation that Carnatic music may be more effective
in enhancing speech understanding abilities compared
to other genres of music. Additionally, the favourable
phonetic characteristics of the Kannada language may
contribute to the manifestation of the effects of mu-
sic training on speech understanding. However, further
exploration is necessary to investigate these specula-
tions. Mishra and Panda (2014), also reported a pos-
itive effect of Carnatic music on auditory perceptual
abilities, observing improved auditory perceptual abili-
ties in Carnatic musicians compared to non-musicians.
Each Carnatic music raga has unique ascending and
descending musical patterns, distinguished by varia-
tions in the pitch of the notes. The ragas chosen for
this study included all seven notes of music, known as
Sampoorna ragas in Carnatic music. Indian classical
music experts recognize the distinct properties of each
raga, such as the tonic frequency, Swaras, Arohana (as-
cending notes), Avarohana (descending notes), Vaadi
(primary note), Samvaadi (secondary note), and more.
Each raga follows specific rules that define its char-
acteristics and set it apart from others. While some
ragas may share the same set of notes or Swaras, their
combinations differ. The ascending pitch sequence is
known as Arohana, while the descending sequence
is called Avarohana. The fundamental basis of dif-
ferentiation lies in the frequency and corresponding
pitch. Unlike Western music, Indian musical notes do
not adhere to standardized frequencies. Instead, artists
choose a convenient frequency as a reference, which
serves as the base for the entire raga. The ragas se-
lected for this study differed from one another by one
8Archives of Acoustics – Volume 49, Number 1, 2024
or two notes, with these differing notes falling in fre-
quencies close to each other. Due to these unique qual-
ities of Carnatic music ragas, they possess a higher
potential as effective tools for auditory training.
7. Conclusion
The ability to perceive and distinguish important
cues such as timber, pitch, and timing is critical in
processing complex signals like speech and music. De-
veloping precise auditory discrimination skills is vital
for effectively extracting these cues. Musical training
plays a crucial role in refining these skills and strength-
ening the neural representation of the auditory sys-
tem, thereby enhancing speech perception. The present
findings suggest that even a short period of musical
training can significantly improve the speech percep-
tion abilities of older adults, especially in challenging
listening conditions. Furthermore, the enjoyable nature
of music further underscores its potential as a valu-
able tool for enhancing speech perception skills in ad-
verse listening situations for older adults. However, the
long-term sustainability of the training effect cannot be
determined solely based on the current study, calling
for further research on the long-term maintenance of
short-term training outcomes.
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