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HomeBank: An Online Repository of
Daylong Child-Centered Audio Recordings
Mark VanDam, Ph.D.,
1
Anne S. Warlaumont, Ph.D.,
2
Elika Bergelson, Ph.D.,
3
Alejandrina Cristia, Ph.D.,
4
Melanie Soderstrom, Ph.D.,
5
Paul De Palma, Ph.D.,
6
and Brian MacWhinney, Ph.D.
7
ABSTRACT
HomeBank is introduced here. It is a public, permanent,
extensible, online database of daylong audio recorded in naturalistic
environments. HomeBank serves two primary purposes. First, it is a
repository for raw audio and associated files: one database requires
special permissions, and another redacted database allows unrestrict-
ed public access. Associated files include metadata such as participant
demographics and clinical diagnostics, automated annotations, and
human-generated transcriptions and annotations. Many recordings
use the child-perspective LENA recorders (LENA Research Foun-
dation, Boulder, Colorado, United States), but various recordings
and metadata can be accommodated. The HomeBank database can
have both vetted and unvetted recordings, with different levels of
accessibility. Additionally, HomeBank is an open repository for
processing and analysis tools for HomeBank or similar data sets.
HomeBank is flexible for users and contributors, making primary
data available to researchers, especially those in child development,
linguistics, and audio engineering. HomeBank facilitates researchers’
access to large-scale data and tools, linking the acoustic, auditory,
and linguistic characteristics of children’s environments with a
variety of variables including socioeconomic status, family character-
istics, language trajectories, and disorders. Automated processing
1
Department of Speech and Hearing Sciences, Elson S.
Floyd College of Medicine, Washington State University,
and Spokane Hearing Oral Program of Excellence
(HOPE), Spokane, Washington;
2
Cognitive and Informa-
tion Sciences, University of California, Merced, California;
3
Department of Brain and Cognitive Sciences, University of
Rochester, Rochester, New York;
4
Laboratoire de Sciences
Cognitives et Psycholinguistique (ENS, EHESS, CNRS),
Departement d’Etudes Cognitives, Ecole Normale
Superieure, PSL Research University, Paris, France;
5
De-
partment of Psychology, University of Manitoba, Winni-
peg, MB, Canada;
6
Department of Computer Science,
School of Engineering and Applied Science, Gonzaga
University, Spokane, Washington;
7
Department of
Psychology, Carnegie Mellon University, Pittsburgh,
Pennsylvania.
Address for correspondence: Mark VanDam, Ph.D.,
Department of Speech and Hearing Sciences, Elson S.
Floyd College of Medicine, Washington State University,
412 E. Spokane Falls Boulevard, Spokane, WA 99202
(e-mail: mark.vandam@wsu.edu).
Automating Child Speech, Language and Fluency
Analysis; Guest Editor, Brian MacWhinney, Ph.D.
Semin Speech Lang 2016;37:128–142. Copyright
#2016 by Thieme Medical Publishers, Inc., 333 Seventh
Avenue, New York, NY 10001, USA. Tel: +1(212) 584-
4662.
DOI: http://dx.doi.org/10.1055/s-0036-1580745.
ISSN 0734-0478.
128
applied to daylong home audio recordings is now becoming widely
used in early intervention initiatives, helping parents to provide
richer speech input to at-risk children.
KEYWORDS: Databases, speech production, automatic speech
recognition, language acquisition, child language
Learning Outcomes: As a result of this activity, the reader will be able to (1) explain the need for a central
repository of daylong family and child audio data and tools and (2) summarize the contributions of the
HomeBank database to the scientific and research communities.
To study the speech and language of
children and families in a natural context is
expensive in terms of the time and effort
required to obtain and code raw audio (and
video) data. One approach to reduce the cost of
data collection is to employ miniaturized,
wearable recording devices and subsequent
automated speech processing algorithms for
processing the raw audio. Recent applications
of this approach have led to a proliferation of
child and family speech data. However, we
have been lacking a central location from which
to access and analyze this wealth of new data.
The project described in this report addresses
this problem directly by offering a central,
public repository of daylong family audio re-
cordings collected from the perspective of the
child, along with the tools needed to analyze
those recordings.
Research on child speech and vocal de-
velopment solidified in the middle of the 20th
century.
1–3
As described by Oller,
4
philo-
sophical changes to the approach of studying
child language developed in the 1970s, result-
ing in widespread attention to child speech
and early sound production,
5–7
and the char-
acteristics of maternal speech and mother–
infant interactions.
8
The goal of much of this
research was to describe and better under-
stand language development starting in in-
fancy. The bulk of the raw data was collected
in laboratories or homes using microphones
and tape recorders to document specific in-
teractions, short samples, or a few partici-
pants. For example, in one early study,
continuous 24-hour recordings were collected
in the homes of six families with infants 6 to
16 weeks of age.
9,10
The goal of the study was
to characterize speech productions of mothers
as they interacted with their infants. Manual
transcriptions were then made from random
segments of the daylong recordings, coding
for certain utterance types. The findings of
this research explored how mothers used
language in their home environment to en-
gage their infants. In another study, Kenyan
preschoolers were recorded for 2-hour seg-
ments in their home environment using a
small body-worn microphone.
11,12
After
transcribing the recordings, Harkness found
that children who talked more with adults had
faster acquisition of language and more lin-
guistic advances.
12
Another study used a radio
microphone worn by preschool children to
collect speech and environmental audio data
over the course of a day.
13
The recordings
were collected using a predetermined sched-
ule of 90-second segments at 20-minute
intervals throughout a 9-hour day. The raw
recordings were natural, but the coarseness of
the automatic recording schedule provided
little context for the content of the collected
raw audio data. Nevertheless, this work laid a
foundation for how linguistic quantity and
quality of parent–child exchanges influence
educational outcomes. Another well-known
study begun in the 1980s recorded 42 families
with 1- and 2-year-old children for an hour
each month in their homes.
14
The data col-
lection for this study lasted 2.5 years, and it
took another 6 years to analyze, code, and
transcribe all the material.
15
This work
showed that vocabulary growth and word
learning are linked to social factors of the
families. Specifically, higher family socioeco-
nomic status was associated with larger
HOMEBANK/VANDAM ET AL 129
vocabularies and improved test performance
on standardized language tests, as well as with
greater quantities and richness of parental
language input to children.
METHODOLOGY OF CHILD
LANGUAGE RESEARCH
Child language samples are typically collected
either in a laboratory setting or during scheduled
visits by researchers to children’s homes. In both
cases, researchers attempt to elicit productions
from children through special games, tasks, and
questions. Recordings made in this way are
subject to concerns regarding ecological validity
and possible biases introduced by the interven-
tions. Furthermore, until recently, the data col-
lected in the laboratory or in the home were
subject to severe hardware, software, quality, and
storage limitations. Unless substantial effort was
put forth, as in the Hart and Risley project lasting
nearly a decade, the resultant recordings most
often consisted of decontextualized small samples
or single-case study designs, limiting the gener-
alizability or extensibility of the findings.
15
Recent technological advances in computer
hardware and software have dramatically
changed the landscape of child language devel-
opment research. It is now possible to collect
daylong audio recordings via small, wearable
recorders placed directly on participating chil-
dren. These recordings can be collected in the
child’s natural environment, providing large,
ecologically valid samples of data with consis-
tent formats across laboratories and researchers.
Machine learning algorithms designed to oper-
ate on these data provide automated detail of
certain aspects of the child’s productions (e.g.,
estimates of total syllables produced or total
conversational exchanges), the child’s linguistic
environment (e.g., amount of overlapping talk,
gender of adult interlocutors, and so on), and
the ambient acoustic environment (duration
and amplitude of TV/electronic media, silence,
noise). For certain measures, acoustic process-
ing is entirely automated, providing results in a
fraction of the time required for a human to
transcribe such data. For example, a daylong
audio recording (16 hours) collected passively
via a recorder tucked into a pocket on a pre-
school child can be uploaded to a computer and
analyzed with speech processing software in
under 2 hours. The output includes files with
the original audio and a time-aligned, tagged
annotation indicating the specified output of
the algorithm. Although modern automatic
annotation methods are not free from their
own weaknesses—the segmentation and diari-
zation of speakers is, of course, not error free—
they have opened new perspectives on child
speech and vocal development, family dynam-
ics, and clinical assessment and intervention.
AUTOMATIC METHODS OF DATA
COLLECTION
The most prominent, pioneering system for
collecting and analyzing daylong audio of children
and families in their natural, usually home, envi-
ronments is the Language ENvironment Analysis
(LENA) system, first developed in the mid-2000s
by the LENA Research Foundation (Boulder,
Colorado, United States). The LENA system is
currently in use by over 200 universities, school
districts, hospitals, and other research institutions
(http://www.lenafoundation.org/lena-pro/). An-
other system for gathering daylong home audio
recordings is the Human Speechome Project,
which started as a case study of a single child.
16,17
To date, the Human Speechome Project has
published some findings and transcription
tools,
17,18
buttherawdataarenotavailablefor
use by other researchers, in large part due to
privacy concerns. In addition to these systems,
there are several researchers who have developed
their own tools for collecting and processing
recordings within their own laboratories or re-
search settings, but many have not been made
publically available or have only been partially
described in the literature.
Research utilizing automated analysis of
daylong audio recordings using the LENA
system is advancing our understanding of au-
tism spectrum disorders,
19–22
childhood hear-
ing loss,
23,24
the consequences of premature
birth,
25,26
the role of television viewing in
development,
27–30
and more. These studies
using the LENA system have utilized thou-
sands of hours of audio data.
The technology is also increasingly used in
applied settings, for example in the Providence
Talks,
31
Project Aspire,
32,33
and the Thirty
130 SEMINARS IN SPEECH AND LANGUAGE/VOLUME 37, NUMBER 2 2016
Million Words initiatives.
34
In these projects,
the LENA system is being deployed to examine
the effects of poverty, hearing loss, and rehabil-
itation efficacy on young children’s developing
linguistic systems. These research projects and
intervention initiatives leverage the strengths of
daylong naturalistic recording combined with
automatic speech processing to inform ques-
tions of interest to a wide range of groups,
including medical professionals, early child-
hood educators, policy makers, and politicians.
The totality of the work completed or in
progress, tools produced, and data collected is
not known, and there is no central repository in
which unpublished work, tools, and data are
stored, organized, or made available to re-
searchers and/or the public at large. Such a
central database could be useful not only for
child language and developmental researchers,
but also for those researchers pursuing technical
advances in automatic speech processing in-
cluding engineers, software and hardware de-
velopers, statisticians and data analysts, and
computational modelers.
DAYLONG RECORDINGS: TYPICAL
COLLECTION AND ANNOTATION
Extended or daylong recordings can be collect-
ed and processed using a wide variety of tools.
As described previously, starting at least as early
as the 1970s, daylong audio recordings have
been collected for research purposes. The pres-
ent project has a critical mass of daylong record-
ings using a particular technology, the LENA
system, but a central repository described here,
HomeBank, is designed and intended to be
compatible with any recording and associated
data of extended family audio. Nevertheless,
due to the current paucity of alternatives, the
remainder of this section will be devoted to
describing typical use of the LENA system.
The LENA device contains a single inte-
grated microphone that records unprocessed
acoustic data to onboard solid-state memory.
The self-contained recorder is 168cm
and weighs less than 80 g.
35
Unlike most
wireless recording setups used in laboratory
settings, the recorder does not transmit to a
receiver; instead it stores the entire recording on
the device worn by the child.
Often, the device is worn by the child in an
item of clothing. This allows the recorder to be
worn throughout the day, regardless of the loca-
tion of the child—it can even record during car
rides, trips to the park, and so on. The LENA
foundation markets custom-tailored clothing op-
tions such as vests, which have a pocket sewn into
the front into which the recorder is snapped into
place. These clothing items have the additional
benefits that they have been tested to minimize
noise from rustling of clothing and to protect the
device from spills while being comfortable for the
child and easy for parents to use (cf. recent work
looking at acoustic response characteristics of the
LENA hardware
36
).
In the typical application, a researcher
furnishes a recorder to the parent along with
instructions to turn on the recorder when their
child wakes up in the morning and turn it off
when the child goes to sleep at night. Parents
are told that they can pause the recorder, should
they need to for privacy, and that entire record-
ings can be discarded upon request.
When the hardware is returned to the
researcher with the recording stored onboard,
the audio data then needs to be analyzed. Few
laboratories are performing exhaustive tran-
scriptions given the length of recordings being
gathered, and instead turn to automatized post-
processing. An ideal goal would be to obtain an
orthographic transcription from the acoustic
signal. However, this approach has not been
entirely successful in natural (i.e., ecologically
valid) settings with highly variable vocalizations,
overlapping conversations, and various environ-
mental sounds. Thus postprocessing currently
focuses on a simpler, though still challenging,
automatic segmentation and labeling task: that
of breaking up the continuous acoustic signal
into segments that are labeled by the primary
sound source. This can be done using custom-
written scripts, but many LENA users opt to
employ the LENA software’s automatic labeling
algorithms. The LENA algorithms return an
audio recording broken down into segments
that are assigned a segment label. The labels
are organized into around a dozen higher-level
categories including talker identity (child wear-
ing the recorder, any other child, adult female,
adult male, and human speech overlapped with
other speech or nonspeech noise), other sound
HOMEBANK/VANDAM ET AL 131
sources (TV or other electronic sounds, noise),
and silence. The result of this processing is
output as a computer text file typically ranging
from 20,000 to 50,000 segments per daylong
recording. Each line of the output file contains
the onset and offset times of the segment, the
specific label, and other information such as
mean amplitude of the segment. The LENA
algorithm output does not provide a written
transcription of the words on the recording
using automatic speech recognition of the sort
found in smartphones. Rather, the LENA
system exhaustively segments the recording
and provides speaker labels and other catego-
rizations for each segment using categories from
the algorithm’s predetermined list.
37,38
There are other types of information that
may be derived automatically from daylong
recordings. For instance, one can draw estimates
of key elements within the child and adult
vocalizations, such as the number of words
spoken by adults and speech-related child vocal-
izations (e.g., speaking, babble, singing) versus
non–speech-related child vocalizations (e.g., cry-
ing, laughing, burping), and whether there are
sequences of segments where the key child and
an adult alternate (which can be automatically
labeled as “conversational turns”). Finally, the
LENA system in particular draws from a stan-
dardized database to provide even more infor-
mation by comparing the acoustic features of the
child’s speech against norms from the Natural
Language Study.
39
This allows the LENA soft-
ware to provide users with an automatic vocali-
zation assessment that aims to provide
information about the child’s developmental
level with regard to speech production ability.
40
The automatic vocalization assessment necessar-
ily relies on normed data, but all other data relies
on standard speech technology methods. For
example, the LENA system uses a combination
of Gaussian mixture models and hidden Markov
models to obtain the talker labels and the child
speech-related versus non–speech-related la-
bels.
41–43
It uses the open source Sphinx software
(CMU Sphinx, Carnegie Melon University,
Pittsburgh, PA) as inputs in estimating number
of adult words and in producing the child
vocalization assessment.
39,44,45
Supplementing the raw audio and the
time-aligned record of labeled segments, several
research teams have developed additional soft-
ware tools that process some or all of these
outputs (i.e., the audio file or the text file
record) for further analysis.
21,46–48
Several of
these researchers have also created practical
software tools ranging from database and file
management to algorithms for acoustic analy-
ses. The tools are undoubtedly useful to the
researchers and their teams, but there was, prior
to HomeBank, no central repository that would
benefit other researchers or those interested in
becoming familiar with the technology. A
comprehensive database of external software
tools would greatly benefit the research com-
munity by reducing the cost of entry and
increasing the accessibility of and proficiency
with the data. This would especially benefit
students and those researchers desiring to take
advantage of the LENA system but not cur-
rently active.
Data on the reliability of such systems is
beginning to emerge, and again the LENA
system is the best validated at present. The
LENA labels and adult word counts compared
with human coders has been published for typi-
cally developing children learning En-
glish,
41,42,48,49
Spanish,
50
Dutch,
51
and French.
52
THE PROBLEM OF DISTRIBUTED
AND SEQUESTERED DATA
Using modern technological resources of auto-
mated data collection, and in particular the
growing popularity of the LENA as a research
tool, research teams around the world are be-
ginning to amass data sets of naturalistic day-
long audio that are vastly larger and more
representative than what is currently available
in either the Child Language Data Exchange
System (CHILDES) repository
53
or Data-
brary.
54
However, because recordings are col-
lected in natural settings and generally involve
families engaged in the full range of typical
activities and interpersonal interactions, public
dissemination of the raw data raises concerns
about violating the privacy of participating
families. These privacy concerns combined
with the extended duration of the audio record-
ings have made vetting of the recordings and
removal of private information a central, but
particularly labor-intensive, concern. As a result,
132 SEMINARS IN SPEECH AND LANGUAGE/VOLUME 37, NUMBER 2 2016
most of the collected data—estimated to be in
the tens of thousands of daylong audio record-
ings—remains sequestered in individual labora-
tories. Indeed, some researchers may be required
(e.g., by their institutional review board [IRB])
to delete the underlying audio data, not having a
system at their disposal to properly vet, store, or
secure sensitive recordings.
The development of a system for sharing
these rich data collections offers a benefit to the
fields of child language acquisition and auto-
matic speech processing. Such a database sup-
ports the sharing and development of resources
for basic research as well as for educational and
clinical work. Facilitating the development of a
system for sharing this valuable data as well as
improvements on and extensions to the existing
analysis tools are the key motivations for the
HomeBank project.
INTEGRATION OF HOMEBANK
INTO TALKBANK
The system described in this report, HomeBank
(available at homebank.talkbank.org/), was de-
signed to be a public repository for daylong
family audio recordings, associated files and
processing output, and software or algorithm
tools for processing data. HomeBank is inte-
grated into the existing framework of the Talk-
Bank databases (which include, for example, the
CHILDES database) and tools entailed there.
HomeBank thus leverages the massive data and
advantages of automatic processing described
previously with an existing infrastructure and
long history of success of the TalkBank project.
Before describing the HomeBank project in
detail, CHILDES and TalkBank will be briefly
introduced.
CHILDES is a Web-based system for
sharing and analyzing child language transcript
data.
53
The database at childes.talkbank.org
includes 50 million words of transcript data,
much of it linked on the sentence level to
digitized video or audio recordings that can
be played back directly over the Web. Over 95%
of the data in CHILDES are publicly available
for downloading and analysis without a pass-
word. CHILDES is one component of the
larger TalkBank system (talkbank.org) that
includes additional databases for the study of
aphasia, traumatic brain injury, second lan-
guage acquisition, dementia, conversation anal-
ysis, meetings, and other language areas.
55
CHILDES began in 1984 with support
from the MacArthur Foundation and has
received continuous support from the
National Institutes of Health since 1987
and from the National Science Foundation
between 1999 and 2004 and between 2015
and 2019. There are currently 1,800 users of
CHILDES located in 35 countries. A search
at scholar.google.com reveals 5,482 articles in
EnglishthathavemadeuseofCHILDES
data or programs. However, because this
inventory does not include research papers
published in other major languages, the actual
size of the research literature generated by
CHILDES is closer to 6,800 publications.
Publications based on CHILDES touch on
every major issue in child language, from
phonology to intellectual development. The
data are most heavily used by researchers in
linguistics, psychology, computational lin-
guistics, speech and hearing sciences, sociol-
ogy, education, and modern languages.
CHILDES and the more general TalkBank
system of which it is a component have adopted
rigorous international standards for data preser-
vation, documentation, and access. In recognition
of this, TalkBank has received the Data Seal of
Approval, based on accurate adherence to a set of
16 standards regarding corpus documentation
(childes.talkbank.org/manuals/), consistent data
formatting in a tightly controlled XML schema
(talkbank.org/software/xsddoc/), metadata gen-
eration in the Open Language Archives Com-
munity (OLAC; http://www.language-archives.
org/) and Component MetaData Infrastructure
(CMDI; clarin.eu) formats, articulation of a full
mission statement, IRB protection (talkbank.org/
share), data storage, long-term preservation,
Open Archives Institute harvesting of CMDI
and OLAC metadata, persistent digital object
identification through the Handle System (han-
dle.net), backup systems (mirror sites, archiving,
git, and so on), statement of codes of conduct
(talkbank.org/share/ethics.html), and proper
treatment of copyright (CC BY-NC-SA 3.0).
TalkBank and CHILDES are also members of
the international Common Language Resources
and Technology Infrastructure (CLARIN)
HOMEBANK/VANDAM ET AL 133
consortium of national language data centers
(clarin.eu).
In addition to these achievements as a stable
center for the sharing of language data, Talk-
Bank has developed standards, programs, and
practices that make it ideal as development site
for the HomeBank project. CHILDES pro-
vides comprehensive software for analysis of
phonological, morphological, syntactic, and dis-
course features, much of it fully automated.
These programs include Computerized Lan-
guage ANalysis (CLAN)
53
for transcript analy-
sis and Phon for phonological analysis,
56
both
with linkages to Praat,
57
a free acoustic analysis
software, with special tools for corpus analysis.
58
CHILDES has 30 years of experience in setting
up alternative levels of data access (talkbank.org/
share/irb/options.html) that protect individual
privacy in accord with high-level IRB standards.
HOMEBANK
HomeBank (homebank.talkbank.org) was con-
ceived to offer researchers greater access to raw
data and tools associated with the rapidly
growing amount of daylong audio files being
collected by a wide range of researchers. Home-
Bank is an effort to address the issue of provid-
ing a centralized database or repository for
daylong audio files, the associated data and
metadata with those files, and software tools
for researchers. We expect that the database will
be useful to a wide range of users, including
those interested in language and child develop-
ment in the social sciences and those interested
in automatic speech processing.
HomeBank consists of a recording data-
base and a code repository. The recording
database consists of vetted and unvetted day-
long audio recordings. In the vetted section,
original daylong recordings and their associated
metadata were vetted by experienced, trained
listeners to ensure that recordings contained no
private or personally identifying information.
For example, if a parent recited her name and
address on a recording, that audio portion is
redacted and inaccessible to the user. The vetted
database is unrestricted and open to the public
for download, playback, or analysis. However,
because the vetted database requires a trained
person to listen to the audio in its entirety, and
requires that individuals on the recording agree
to a more open distribution, this is a relatively
modest-sized database.
The other part of the recording database is
a restricted database requiring special permis-
sions to access. This part of the database con-
tains unvetted audio recordings and their
associated metadata, and recordings for which
public distribution has not been agreed to by the
participants. Because much of the audio has not
been vetted and cleaned by trained listeners, this
can be a much larger database than the unvetted
database. However, because personally identi-
fying details may be contained on the record-
ings, several tight user restrictions have been
implemented to safeguard the participant fam-
ilies. These recordings are stored in a password-
protected computer space only available to
registered HomeBank members who have
agreed to confidentiality in writing and have
passed recognized ethical training on dealing
with human data. HomeBank is intended to be
accessible and open to the research community
while balancing the collective obligation to
treating participants ethically. Additional de-
tails can be viewed on the HomeBank Web site
or by direct email inquiry (contact information
at homeBank.talkbank.org).
Metadata are an important aspect of the
database that greatly benefits from the extant
tools developed from the TalkBank project. In
addition to the source audio files and results of
the LENA speech processing algorithms, data
for each recording generally includes child age,
child sex, information about whether the child
is typically developing or is a member of a
specific clinical population (e.g., having hearing
impairment, language delay, autism spectrum
disorder, and so on), education level of the
child’s primary caregivers, country the child
was recorded in, dominant ambient language,
scores on language or other developmental tests
or questionnaires, and comments on the re-
cording. The metadata protocol is designed to
be flexible and extensible within the database.
For example, certain characteristics of children
with autism spectrum disorder may warrant
several data fields unique to that population,
or children with hearing loss may have audio-
logical data such as audiograms or details of the
hearing aid itself.
134 SEMINARS IN SPEECH AND LANGUAGE/VOLUME 37, NUMBER 2 2016
One especially important category of meta-
data are a substantial and growing body of
human-generated codes for the human analysis
of transcripts (CHAT) transcriptions from
portions of daylong recordings. Although au-
tomatic labeling is valuable because it is com-
puted without supervision and can be efficiently
applied to the entire data set, it has several
limitations. First, the labels are not as accurate
as those that can be made by human listeners.
Second, the labels only provide very basic
information about the events in the recording.
Many researchers are interested in not only
when individuals are vocalizing but also in
the phonetic, prosodic, linguistic, or semantic
content of those vocalizations. Many speech
researchers are familiar with traditionally tran-
scribed corpora, but have less experience inter-
preting machine-generated labels. Thus, the
transcriptions are an especially valuable aspect
of the database specifically for an important
expected user base. In addition, human tran-
scriptions are the gold standard by which
machine algorithms are both evaluated and
trained, and so are essential to the development
of new and improved speech recognition tech-
nologies in this domain. We expect that because
these gold standard, human-generated tran-
scriptions exist, researchers interested in auto-
matic speech processing will be attracted to the
database.
The second part of HomeBank is an open
source code repository, HomeBankCode,
hosted at GitHub (github.com/HomeBank-
Code). GitHub provides free storage and
version history. It also has the advantage of
being extremely widely used across academia,
industry, and hobbyists, making it likely that
many potential contributors are already fa-
miliar with how to use Git and GitHub and
increasing the chances that other users will
discover the resource. The repository is pub-
licly available and adheres to the open source
philosophy. Code, pseudocode, and stand-
alone algorithms are posted by users, to be
modified, improved, changed, or used as the
basis for new code by other users. For exam-
ple, users have developed tools to process
LENA daylong audio files, including tools
for applying acoustic analyses in batch, per-
forming data cleaning, using transcription
functions within CLAN, and deidentifying
Interpreted Time Segments (ITS) files. These
scripts have been shared through HomeBank
for public use and extension. This is the
primary source of user-created, postprocess-
ing tools for use on the daylong recordings.
Additionally, the HomeBank website also
maintains contact information, an overview of
the project, links to related resources on the
Web, and samples of related documents such as
help with construction of IRB and consent
forms for prospective researchers and how to
organize metadata.
Anyone with access to the World Wide
Web has unrestricted access to the vetted
public database and HomeBankCode reposi-
tory via the homebank.talkbank.org Web site.
Before users can gain access to the larger
protected database, they provide written evi-
dence of ethical training (e.g., Collaborative
Institutional Training Initiative (CITI) certif-
icate), and oral and written agreement of data
use and confidentiality, obtained through a
HomeBank staff person. Upon registration of
membership, the member and supervisees, such
as students or laboratory staff registered as
working under that member, gain access to
the database containing more restricted and
unvetted files.
Finally, the HomeBank database is com-
mitted to participant respect and beneficence.
59
We have constructed consent form templates
that allow parents to opt in to sharing their data
in HomeBank. Permission to post the record-
ing is requested at the time of each recording, or
some researchers may opt to procure retroactive
participant consent to post extant recordings in
support of HomeBank. For data to be contrib-
uted to HomeBank, consent forms should ask
parents to indicate that they approve that their
child’s daylong audio recordings and metadata
are included in a shared database and that they
are comfortable with the public being able to
listen to their recordings. Participants could
alternatively indicate that their data are to be
made available only in the restricted data set, for
download by more thoroughly vetted research-
ers only. The consent forms can also provide
contact information should they decide at a
later date that they wish to revoke access to their
data.
HOMEBANK/VANDAM ET AL 135
WHAT HOMEBANK BRINGS TO THE
COMMUNITY
HomeBank provides contributors (of record-
ings, other data, or code) with a way of aug-
menting their impact in the research
community. Following the model of the
CHILDES database, all users of data from
either the public or the restricted data sets
will be provided with the appropriate citations
and be required to cite them in any publications
that utilize the data sets. CHILDES has placed
consistent emphasis on the importance of citing
original sources, with excellent results.
CHILDES also maintains methods for citing
corpora as publications through assignment of
ISBN codes and Handle System identifiers; this
policy is extended to HomeBank data as well.
Code in the HomeBankCode GitHub reposi-
tories is licensed according to the contributor’s
preferences; for example, contributions to date
use the GNU General Public License, version
2, requiring that any derivative work also make
source code freely and publicly available.
The data in HomeBank provide an impor-
tant resource to child development researchers.
In contrast to in-laboratory experiments or
short home recordings, daylong recordings of
children and their caregivers in their natural
environments provide more holistic informa-
tion about child development. They provide a
window into all types of children’s daily expe-
riences, as well as the ability to better estimate
the frequency of different types of events and
experiences over the course of a child’s day.
Many researchers, including student research-
ers, may have questions that can be addressed by
human or automated coding of these daylong
naturalistic samples, but lack the time, training,
or funds to obtain a large number of original
LENA recordings. Even if they do have the
resources, those resources may not best be spent
on replicating data that has already been col-
lected and archived in the HomeBank database.
Furthermore, a very large database may
allow researchers to ask questions of the data
that would not otherwise be possible in a
smaller database. For instance, users may train
listeners working under their supervision to find
segments of the recording in which events of a
particular type (e.g., singing or book reading)
are present and manually transcribe those
events, or perform acoustic analyses on selected
segments or in certain contexts. Alternatively,
users may apply their own automated data
analysis methods to a large number of the
recordings without necessarily listening to the
recordings except to establish reliability. An-
other advantage of daylong recording is that,
assuming there is a way to efficiently process the
large quantity of data, it is possible to accumu-
late data on relatively rare events. In all of these
cases, the presence of a well-populated Home-
Bank database ensures that hard-earned day-
long recording data, generously contributed by
parents and children for the purposes of in-
creasing our understanding of child develop-
ment, is put to maximum use.
The raw WAV files included in HomeBank
also provide an ample, large data set for input to
supervised or unsupervised machine learning
systems. For example, unsupervised deep learn-
ing neural network algorithms are increasingly
being recognized as powerful methods for ex-
tracting acoustic features for automatic speech
analysis.
60
Such approaches greatly benefit from
more and more naturalistic data, which is exactly
what HomeBank can provide.
FUTURE DIRECTIONS FOR
HOMEBANK
HomeBank was launched in 2015 with support
from the National Science Foundation through
2019. TalkBank has agreed to partner with and
host HomeBank in perpetuity. The key develop-
ments required to maximize HomeBank include
increasing the size and variety of the database and
making researchers aware of the tool.
There are several developments that will
make HomeBank even more useful. First, we
may add a third section in the recordings
database for daylong recordings that were col-
lected under maximally restrictive sharing con-
ditions, whereby no users outside of the initial
laboratory where collection occurred are al-
lowed to listen to them. This might happen,
for instance, if they involve families who are
particularly concerned with privacy or popula-
tions at risk. Naturally, such recordings could be
of little use unless another feature was added to
HomeBank, namely the development of a sys-
tem where users cannot access underlying raw
136 SEMINARS IN SPEECH AND LANGUAGE/VOLUME 37, NUMBER 2 2016
data, but can run queries over it using a scripting
interface.
Second, the fact of creating a common
repository will allow the community to pool
information and gain from the common knowl-
edge. For instance, this common resource
would allow the development of even more
accurate norms in an open-source format. As
mentioned previously, only the LENA system
provides users with an estimation of the child’s
production skills, because only they have made
the investment of developing norms, and this
only for a representative American sample. As
the recording repository in HomeBank grows,
new norms can be derived not only for Ameri-
can recordings, but also for those in other
countries (provided that researchers there con-
tribute to HomeBank).
Similarly, speech technologists would be able
to use the HomeBank recordings to improve the
current automatic labeling algorithms. The tran-
scriptions within HomeBank data could be used
as labels for training supervised learning systems
and for evaluation of automatic speech processing
systems. Furthermore, the availability of the audio
recordings to approved groups of listeners would
enable anyone with the human resources neces-
sary to contribute to providing additional labels
that could be used for training their own systems
and that would ideally be reshared back to the rest
of the community using the recordings. These
two examples illustrate the virtuous circle that
could be established between the child language
and the speech technology community, in essence
providing the full set of daylong audio recordings
for approved users would provide valuable data to
speech processing engineers. This would presum-
ably help them generalize their methods to other
types of naturalistic child data. The development
of better speech processing methods trained on
naturalistic speech would in turn benefit the child
development community, because better auto-
mated analysis tools will enable more efficient
and higher quality research and assessment.
EXAMPLES OF PROJECTS FOR
WHICH HOMEBANK COULD BE
UTILIZED
In this section we briefly describe several proj-
ects, both in general terms and specific projects
that would benefit from the HomeBank data-
base. These are selected projects intended to
demonstrate the broad utility of HomeBank.
Acoustics of Child and Adult
Vocalizations
Several HomeBankCode extensions focus on
acoustic analyses to get a more detailed under-
standing of the sounds the children and adults are
producing. For example, Oller and colleagues
analyzed child vocalizations by first segmenting
them on the basis of amplitude contours into
“vocal islands,”
20
which roughly corresponded to
syllables, and then analyzed those vocal islands
according to duration, spectral tilt, and various
other acoustic features. Oller and colleagues then
subjected those acoustic features to principal
components analysis and used the principal com-
ponents as inputs to classifiers of clinical group
membership. They found that the approach could
reliably discriminate the recordings of children
with autism from those of nonautistic children
with developmental delays and from those of
typically developing children.
Other research teams have been combining
the output of LENA with acoustic analysis
tools available in Praat and custom routines in
MATLAB, Python, and R to obtain acoustic
information (pitch, vowel formants, spectral
characteristics, amplitude, and so on) for child,
female adult, and male adult vocalization seg-
ments.
57
One example of a current direction of
this work is characterizing the acoustics of
child-directed speech (CDS) and adult-direct-
ed speech during the naturalistic interactions
represented in the home recordings. Character-
istic CDS has increased pitch, extended syllable
and word durations, exaggerated prosody, re-
stricted syntax, and greater phonetic variabili-
ty.
5,8,61,62
Although CDS in general has had
steady attention in the literature at least since
the early 1970s, CDS is receiving renewed
attention recently due in part to the availability
of LENA. This in turn has renewed discussions
about ecological validity in this domain. For
example, some studies have found that fathers
and mothers differ in the speech they direct
toward their children.
63–67
This possibility has
been examined recently using a very large
database of LENA recordings, showing that
HOMEBANK/VANDAM ET AL 137
in daylong, ecologically valid samples, com-
pared with mothers, fathers used fewer pitch
fluctuations,
47,68–70
a greater variety of lexical
items, and more complex syntactic forms. An-
other recent study used LENA recordings and
LENA-generated annotated output as the in-
put to doing acoustic analysis in Praat.
71
They
found similarities in pitch between mothers and
their children, associations between temporal
contingencies in conversational exchanges be-
tween mothers and children, and acoustic con-
vergence of pitch across conversational blocks of
mothers and children.
Characterizing Child–Adult Interaction
Dynamics
At a higher temporal level of analysis, research-
ers have developed tools that use the onset and
offset times of child and adult vocalization
segments as identified by the LENA software
to give a richer picture of the overall pattern of
when children and adults are vocalizing over the
course of the day and how the children’s and
adult’s vocalizations relate to each other tem-
porally.
72,73
This work has, for example, found
that adult vocal responses are more likely when
child vocalizations are speech related, and that a
child is more likely to produce a vocalization
that is speech related when the child’s own most
recent speech-related vocalization received a
response.
21
Furthermore, various components
of this feedback loop were found to vary for
children of different ages and socioeconomic
backgrounds as well as for children who are
typically developing compared with those with
autism spectrum disorder. Combined with
computational modeling work,
21
the results
provided support for the theory that there is a
positive feedback loop between child behavior
and reinforcing adult responses that helps sup-
port children’s speech development, and that
differences in the feedback loop can have cas-
cading effects on the child’s overall develop-
mental trajectory.
74,75
These results provide
examples of how automatic speaker identifica-
tion within daylong home audio recordings can
provide the quantity of time series data needed
to detect the presence of a two-part feedback
loop. The code for these analyses was provided
as supplemental material to the article and is
now available via the HomeBankCode reposi-
tory on GitHub.
Development of New Tools for
Interacting with Data
Other tools being developed include those that
make human listening tasks more efficient.
Researchers have developed tools for automati-
cally extracting audio segments specified in the
LENA output files and the associated WAV
file, playing all the sounds from a given talker to
the user, and allowing the user to provide
feedback, for example on whether or not the
sound was assigned the correct speaker label by
the LENA algorithm.
49
Other research teams
have used hybrid machine–human transcription
techniques to test the automatic labeling pro-
cedures.
43
Some research groups have further
developed custom software for audio extraction
and playback, and human listener judgment
collection, acoustic analysis, database manage-
ment, and statistical analysis.
23,27,47
A major
advantage to this type of specialized playback
software is that it enables much more efficient
human coding of the data. VanDam and Silbert
reported on human judgments of over 90,000
exemplars of speech segments labeled by the
automatic methods of the LENA system, with
accuracy/validity compared for different label
types.
49
They found that the automatic meth-
ods perform similarly to other state-of-the-art
automatic speech recognition methods, but that
performance varies for different labels (adult
men are more accurately labeled than adult
women, for example). Acoustic analyses showed
that temporal and spectral qualities—but not
amplitude—interact in complex ways in the
automatic label determination process. This
data extraction, playback, and acoustic analysis
software has been explicitly developed in a
format suitable for sharing, extensibility, docu-
mentation, and modification.
DISCLOSURES
None of the authors have conflicts of interest.
ACKNOWLEDGMENTS
This work was supported by a collaborative
National Science Foundation grant awarded to
138 SEMINARS IN SPEECH AND LANGUAGE/VOLUME 37, NUMBER 2 2016
A.S.W. (1539129), M.V.D. (1539133), and B.
M. (1539010), and by WSU Spokane Seed Grant
Program awarded to M.V.D. It was further
supported by a National Institutes of Health
grant to Bergelson (DP5-OD019812-01).
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