Preschool Children Speech Recognition for Early Childhood Intervention:
Motivation and Challenges
Satwik Dutta and John H.L. Hansen
Center for Robust Speech Systems
The University of Texas at Dallas, Richardson, Texas, USA
satwik.dutta@utdallas.edu, john.hansen@utdallas.edu
Dwight Irvin
Juniper Gardens Children’s Project
The University of Kansas, Kansas City, Kansas, USA
dwirvin@ku.edu
Abstract
Monitoring child development in terms ofspeech/language skills has a long-term impact
on their overall growth. As student diversity
continue to expand in US classrooms, there isa growing need to benchmark social engagement, both from a teacher-student perspective,
as well as student-student content. Given various challenges with direct observation, deploying speech technology can assist in extracting
meaningful information for teachers. These
will help teachers to identify and respond to
students in need, immediately impacting theirearly learning and interest. This study takesa deep dive into exploring hybrid ASR solutions for low-resource spontaneous preschool(3-5yrs) children (with and without developmental delays) speech, being involved in various activities, and interacting with teachersand peers in naturalistic classrooms. For the
purpose of data augmentation, various out-ofdomain corpora over a wide and limited age
range, both scripted and spontaneous wereconsidered. Acoustic models based on factorized time-delay neural networks, and bothN-gram and neural language models were considered. Results indicate that young children
have significantly different/developing articulation skills as compared to older children. Outof-domain transcripts of interactions between
young children and adults however enhances
language model performance. Overall transcription of such data, including various nonlinguistic markers, poses additional challenges.

1

Introduction

Early childhood (Britto et al., 2017) is the formative
years of a child’s developmental skills, which include but are not limited to cognitive, motor, physiology, speech, and language development. On
average, children acquire about 900 words by 24months (Huttenlocher et al., 1991), and show rapid

linguistic development thereafter based on speech
production, vocabulary and grammar knowledge.
Preschool classrooms are viable spaces for supporting language development in young children.
Speech/language development in preschool classrooms is reliant on various natural communication
partners, including both peers and teachers. Children’s speech sounds develop from their first babbles until mid-elementary school (Shriberg, 1993).
Throughout early childhood (birth to 8 yrs), typically developing children are expected to progressively acquire and improve production of speechsounds. Table 1 shows speech sounds that are
expected to be developed in each stage of earlychildhood. When speech production skills are
Table 1: Summary of speech sound development inearly childhood (birth to 8 yrs) in ARPAbet format.
Stage
Age (years)
Speechsoundsexpected
to bedeveloped
for eachstage with
examplesEarly
1 to 3M “mama”
B “baby”
Y “you”
N “no”
W “we”
D “daddy”
P “pop”
HH “hi”

Middle3 to 6 21
T “two”
NG “running”
K “cup”
G “go”
F “fish”
V “van”
CH “chew”
JH “jump”

Late
5 to 7 12
SH “sheep”
S “see”
TH “think”
TH “that"
R “red”
Z “zoo”
L “like”
ZH “measure”

developing, children may omit, substitute or haveinconsistency. Language planning is also evolving,so word selection and grammar may have issues.
Not all children acquire these skills at a similarpace, especially those with speech/language issues.
Early speech/language acquisition delays can affectlong term social and academic outcomes (Kaiser
and Roberts, 2011). Using of direct observations(Irvin et al., 2017) or manual video coding to support teachers working with young children withspeech delays is not a sustainable, nor scalable,
endeavor. Deploying sensor-based speech monitoring tools in classrooms can be of immense help
to teachers in creating and maintaining a rich lan-13m(a) A preschool classroom
map with activity areas.

PlaygroundC
C

PuzzleDiscussion

A

Art

C

C
A
C

C

C

C

Lunch
C

C

A

A

C

C

C

C

Primary Child(wearing
the LENA)

Secondary (near Primary
ChildChild LENA)(c) Speaker annotation based on audio from each LENA device

C

C

Location
Tracker(near PrimaryChild LENA)

A

C

A

8m
Adult

C

A

C
C

C

Books

C
C

C

C

Music
ToiletAC

LENA(d) Custom made sample t-shirt
and location of devices

C

Time of the day(b) Schematic location ofsubjects involved invarious activities
throughout the day.
A

Adult

C

Child

Figure 1: Data collection of Preschool Child-Adult Interactions.

guage environment for all children. Such tools
could provide feedback to allow teachers to better identify children in need of further linguistic
development and support. It is known that developing Automatic Speech Recognition (ASR) systems
for children is far more challenging than for adults(Gerosa et al., 2007), primarily due to various developing factors (e.g., articulation/pronunciation,
physiology/motor skills, vocabulary, and grammar).
Most prior research on child ASR (Stemmer et al.,
2003; Shivakumar et al., 2014; Tong et al., 2017;
Wu et al., 2019; Shivakumar and Georgiou, 2020;
Yeung et al., 2021; Rumberg et al., 2021; Gretter et al., 2021) has focused on older children (615 yrs), with more than 70 hours data collected
in clean/controlled settings, with just one speakerusing prompts or read stimuli, and limited spontaneous speech. To date, limited research has focusedon developing speech processing systems for adultchild interactions in naturalistic preschool settings(3-5 yrs) while they are involved in various activities throughout the day. Moreover, there is lackof publicly available young child speech corpora(primarily due to privacy/regulations). A recent
study (Yeung and Alwan, 2018) also describedvarious challenges in developing ASR systems
for single-word utterances read aloud by kindergarten (5-6 yrs) children achieving a Word Error
Rate (WER) of 25%. Our multi-disciplinary educational research project focuses on quantifying“learning" based on social engagement for use inclassroom settings by teachers. It is based on spontaneous interactions between multiple teachers andpreschool children (3 to 5 years) within naturalistic noisy preschool classroom environments. Priorwork related to this project has primarily considered ‘speaker group’ diarization, by classifying
speech segments from adult vs. children, collectively across multiple classrooms and also for childdirected speech via an adult ASR approach. In thisstudy our primary focus is on developing a robust
ASR system for preschool children taking into account their developing nature and developmental
delays.

2

Corpora2.1

Primary Corpus: Preschool Children

Spontaneous child and adult speech was captured
in preschool classrooms (Fig 1(a)), in a large urban
community in a Southern state in US, using a lightweight compact digital audio recorder (LENA1 ) attached to subjects (Fig. 1(d)). A total of 33 children
aged 3 to 5 years with and without speech/language
delays, and 8 adults teachers participated in thisstudy. For a given session, multiple adults and children were involved in various activities throughout the day. Fig.1(b) shows a schematic diagramof locations of the subjects through various timestamps of the day for a given session. Conversational speech was collected in multiple sessions
over several days in different classrooms with different groups of children. The LENA unit data canbe considered as individual audio stream and wastagged into three speaker (Fig.1(c)) categories: Primary child (speech initiated by child wearing that
LENA unit), Secondary child (speech originatedby any other children within close proximity of primary child), and Adult (speech originated by any
1

https://www.lena.org/

adult in close proximity). It is noted that for each
LENA audio stream, there is only 1 Primary childand multiple Secondary Children and Adults (e.g.,each LENA stream is associated with anonymouschild id). Out of all individual LENA audio streams,
40 streams were used for training (≈ 18 hours) andremaining 8 for test (≈ 4.5 hours). Care was taken
to avoid overlap of the same group of children between train/test. Ground-truth was based on humantranscriptions and only the segments spoken byboth primary and secondary children (will be referred as ‘Preschool’) were considered for ASR
assessment. All participants consented to the useof de-identified data for analysis. This study wasapproved by the Institutional Review Board of both
KU and UTD for analysis.
2.2

Secondary Corpus: OGI, CMU Kids &CHILDESOGI Kids corpus (Shobaki et al., 2000) (≈ 60
hours) contains both prompted and spontaneous
speech of 1100 children between Kindergarten and10th grade, collected using head-mounted microphones while interacting with a computer using
prompts. For the CMU Kids corpus (Eskenazi
et al., 1997) (≈ 9 hours), speech is read aloudby 76 children for an age range of 6 to 11 years using head-mounted microphones. Transcripts fromvarious corpora of the CHILDES (MacWhinney,2014) project were also used. These corpora in
CHILDES, which were identified through a careful
review with the goal of using only those conversations involving younger children (5 yrs or less)and in naturalistic scenarios, included: Braunwald,
EllisWeismer, Gleason, Hall, HSLLD, MacWhinney, McMillan, Peters/Wilson, POLER-Controls,
Sachs, Sawyer, Snow, and Sprott.

3

Experiment Setup

3.1

Data Augmentation

Both OGI and CMU corpora were used for speech
data augmentation. Previous work using either
one or both corpora (Wu et al., 2019; Yeung et al.,2021; Rumberg et al., 2021; Yeung and Alwan,2018) for ASR only considered scripted and not
spontaneous speech. For our study, two sets of
OGI were considered for augmentation: (i)‘OGI
Scripted’: used only scripted speech from a random
sample of children across all ages, and (ii)‘OGI
Kindergarten’(≈ 5 hours): used both scripted andspontaneous speech of children in Kindergarten

from OGI. Spontaneous speech segment/speakerin OGI were ≈2 mins duration each, so these werehand transcribed into shorter segments (10 to 15secs) for ASR experiments. Since both OGI andCMU are clean, compared to our Preschool data,
Musan (Snyder et al., 2015) dataset was used todegrade the audio (in OGI & CMU).
3.2

Acoustic Model (AM) Development

All acoustic model training and decoding experiments were performed using Kaldi (Povey
et al., 2011). For the GMM-HMM systems, Melfrequency cepstral coefficients (MFCCs) (Young,1996) were extracted for every 25 ms window
and 10 ms overlap. 13 MFCCs along with their∆ and ∆∆ features were used as front-end features. The GMM-HMM systems were trained toprovide frame-to-phone alignments for the DNN
based systems. Various acoustic model adaptation
techniques such as: linear discriminant analysis,
maximum likelihood linear transformation estimation and speaker adaptive training were also included in training the triphone GMM-HMM systems for better alignment. The input features to the
DNN-HMM models included a 40-D high resolution MFCCs of current and neighbouring frames
and a 100-D i-vector (Hansen and Hasan, 2015)of the current frame. The i-vectors were calculated by generating speed-perturbed training datawith 3 (0.9,1.0,1.1) speed factors. In addition, thehigh-resolution MFCCs were also replaced with
40-dimensional Mel-frequency Filter Banks Energies (MFBE) (Paliwal, 1999) by Inverse Discrete
Cosine Transform. Factorized time-delay neuralnetworks (TDNN-F)(Povey et al., 2018a), originally proposed as a data-efficient alternative to
TDNN for enhancing ASR performance of lowresource languages with less than 100 hours ofdata, were primarily used as hidden layers for the
hybrid DNN-HMM acoustic models. Apart from
TDNN-F layers, CNN and LSTM layers were also
deployed. A time-restricted self-attention (Vaswani
et al., 2017; Povey et al., 2018b) mechanism (withmultiple heads) was also deployed. Another data
augmentation approach called SpecAugment(Parket al., 2019) was applied directly to MFBEs. It consists of warping the features, masking blocks of frequency channels, and masking blocks of time steps.
Vocal Tract Length Normalization (VTLN) (Eideand Gish, 1996), a speaker normalization techniqueto compensate for varying vocal tract lengths ofspeakers and previously used in developing various
child ASR systems(Stemmer et al., 2003; Shivakumar et al., 2014), was also performed.
3.3

Language Model (LM) Development

In this study, both N-gram and RNN-based language models (LM) were used. All N-gram LMs
were trained using SRILM toolkit (Stolcke, 2002)
and the RNN-based using PyTorch. Four types
of LMs were trained from scratch using the training text: (i) only Preschool data (Sec.2.1), (ii)
Preschool, CMU and OGI-Scripted, (iii) Preschooland OGI-Kindergarten, and (iv) Preschool and
CHILDES. Pre-trained 3-gram and 4-gram LibriSpeech (Panayotov et al., 2015) LMs were also
used. For the RNN-based LMs, we used 2-layer
LSTMs of 605 embedding size and 650 hidden dimension. Dropout was considered to overcome
overfitting. Lattice rescoring (Li et al., 2021)
was used to decode the RNN-based LM. CMU
Pronouncing Dictionary2 was used. Various nonlinguistic markers included: laugh, cough, scream,
gasp, breath, babble, cry, loud music, crowd andplay noise, and other noise.

4

Results & Discussion

4.1

Child ASR Performance

Selected ASR experiment results are summarizedin Table 2, reporting WER on Preschool test speechdata. Exp# A1 shows a triphone GMM-HMM
AM trained on Preschool speech generate a very
high WER of 90.28% for pre-trained 3-gram LibriSpeech LM. Using an 11-layer TDNN-F based
AM, 40 MFCC features and speed-perturbed ivector (of factor 3) in Exp# A2, a much lower WER
of 63.66% was achieved using the same LM thanExp# A1. Replacing the 3-gram with 4-gram LibriSpeech LM, a minor improvement is reported
in Exp# A3. Overall, higher N-grams did not impact WER significantly across all experiments, so
only 3-grams were reported all future experiments.
Similarly, an increased speed perturbation factor of
5 (Exp# A3) also didn’t improve the WER much.
In Exp# B1 (similar to A1 except LM), we notice that by using an in-domain LM, WER drops
to 78.39% as compared to 90.28% in Exp# A1.
Again in Exp# B2 (similar to A2 except LM), we
notice a significant drop of WER to 49.02% ascompared to 63.66% in Exp# A2. Interpolation ofboth the above LMs and rescoring did not improve2

http://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict/

WERs. Using LM trained on spontaneous conversations of preschool children shows a significant
improvement in our study than using pre-trained
LibriSpeech LM, as compared to previous studies(Wu et al., 2019; Yeung et al., 2021) for older children speech where Librispeech LM worked fine.
This signifies that young children do not follow thegrammar/language structure in spoken English or
those similar to adults, while they are still developing such skills the sentences produced by preschool
children will contain various errors such as incorrect grammar, repetitions, etc. In Exp# B3 by replacing MFCCs with MFBEs and increasing thenumber of TDNN-F layers to 17, WER further improves to 47.02%. However in Exp# B4, using
VTLN shows no improvement in WER (47.17%)
for DNN-HMM systems compared to Exp# B3(previous research using VTLN has only shown
improvements for GMM-HMM systems). In Exp#
B5 by adding a SpecAugment layer to MFBEs,
and an AM using a 6-layers of CNN and 9-layersof TDNN-F, WER further reduces to 43.03%. In
Exp# B6 by adding 1-layer of TDNN-F and LSTM,
WER increases to 44.59%. In Exp# B7, by replacing the last TDNN-F+LSTM layer with multi-head
Attention, WER reduces to 42.00%. Previous research (Povey et al., 2018b) had achieved improvements by replacing TDNN+LSTM layers with attention layers for large datasets. Again in Exp# B7,
by lattice decoding of an LSTM-based LM, WER(42.44%) does not improve. LSTM-based LMs
are data hungry, and it seems our Preschool data
does not have enough text. Similar to Exp# B7,
in Exp#s C1 by augmenting older children speech(CMU, OGI Scripted) to Preschool speech WER
of 43.57% was achieved. By augmenting both
scripted and spontaneous Kindergarten children
speech (OGI Kindergarten), also did not improve
WERs as shown in Exp#s D1. These results show
that: (i) age is an important factor while developingchildren ASR, (ii) young children have developing
articulation skills (impacting AM performance),and (iii) developing grammar/language skills (impacting LM performance). Finally by adding theCHILDES transcripts to Preschool in Exp# E1, fortraining an LSTM-based LM and by lattice rescoring we achieve the lowest WER of 39.52% acrossall test subjects. In Exp# E1A, we report WERsof 36.88% and 60.28% for test subjects with andwithout speech/language delays respectively. For
the same ASR engine, children with delays show

Table 2: Child ASR Performance.
#

A1
A2
A3
B1
B2
B3
B4
B5
B6
B7
C1
D1
E1
E1A

Features♣

Acoustic Model
Acoustic Model
Language Model
LanguageWER (%) of
Training Data♠
Training Data♠
Model
Preschool Test
A. Using Preschool (3-5 yr) child speech and pre-trained adult LM
M∆
PS
GMM-Tri3L3-gram90.28
M∆ + I3PS
TDNN-F(11)L
3-gram
63.66
M∆ + I3 vs. I5
PS
TDNN-F(11)L
4-gram
62.00 vs. 61.26
B. Using only Preschool (3-5 yr) child speech
M∆
PS
GMM-Tri3PS3-gram
78.39
M∆ + I3PS
TDNN-F(11)PS
3-gram
49.02
E + I3PS
TDNN-F(17)PS
3-gram47.02
E + I3
PSV T LN
TDNN-F(17)PS
3-gram
47.14
ES + I3PS
CNN(6) + TDNN-F(9)PS
3-gram
43.03
ES + I3PS
CNN(6) + TDNN-F(10) + LSTM(1)PS
3-gram
44.59
ES + I3PS
CNN(6) + TDNN-F(9) + Attn(1)PS
3-gram vs. LSTM
42.00 vs. 42.44
C. Augmenting out-domain children speech over a wide age range (5-15 yrs)
ES + I3
PS + CM + OS
CNN(6) + TDNN-F(9) + Attn(1)PS + CM + OS
3-gram
43.57
D. Augmenting out-domain kindergarten (5-6 yrs) children speech
ES + I3
PS + OK
CNN(6) + TDNN-F(9) + Attn(1)PS + OK
3-gram
42.32
E. Using out-domain naturalistic conversations of young children (5 yrs or less) and adults for LM training
ES + I3PS
CNN(6) + TDNN-F(9) + Attn(1)PS + CH
LSTM
39.52
Test subjects WITHOUT speech/language DELAYS vs. subjects WITH speech/language DELAYS
36.88 vs. 60.28♣ M∆ → MFCC & ∆ & ∆∆, E/ES → Filter-Bank Energy (/with SpecAugment), I3/I5 → 3/5* Speed pert. i-vector♠ PS → Preschool, L → LibriSpeech, CM → CMU, CH → CHILDES, OS → OGI Scripted, OK → OGI Kindergarten

higher WER.
4.2

Child ASR Error Analysis

WER, measured on the best model in Exp# E1,
constituted of 25% substitution and 12% deletion
w.r.t. the total words in test set. The total % oferrors, due to substitution and deletion, and classified by part of speech, consisted of: 45% nouns,
12% verbs, 10% pronouns, 6% prepositions, 6%
adverbs, 4% adjectives, 2% WH-words (what, who,etc.), and 15% others. Out of all substitution errors, 80% were monosyllabic words and remaining
were multi-syllabic. While for deletions, 90% weremonosyllabic words and remaining were multisyllabic. Out of all substitution errors, 38% words
contained at least 1 middle stage speech sound (refer Tab.1), and 43% words with at least 1 late stage
speech sound. Similarly for deletion errors, 37%
words had at least 1 middle and 29% words had atleast 1 late stage speech sounds. Errors arise due tovarious non-linguistic markers (e.g: [gasp]), shownin Fig.2(1,4), otherwise do not change the meaning.
Shown in Fig.2(2,4), words pairs like ‘x-ray’ and‘tray’, or ‘bag’ and ‘bad’ have very similar pronunciations. Similarly, Fig.2(3) shows an error where‘wanted’ was predicted as ‘want it’. In the originalaudio for Fig.2(3), while the child was trying topronounce ‘pizza’, they did utter ‘peek’ before andthereby it can be considered as a transcription error.

5

Conclusions

Developing ASR systems for children is difficult,and even more challenging for younger children,
especially in naturalistic classrooms scenarios. ItΩ happy birthday [gasp] kittyΨ happy birthday *** kittyΩ  Ground TruthΨ  Model Output

Ω kitty cat you have to get a xray
Ψ kitty cat you have to get that tray
Ω somebody brought you apizza just what you *** wanted
Ψ somebody thatyou peek pizza just what you want itΩ there’s a
so many letters today [gasp] if its a letter for kit kitty put itin this borrow bagΨ they’re getting so many letters today *** is its a letter for can kitty put itin this borrow bad

Figure 2: Various error scenarios of model output vs.
ground-truth.

is not possible to relate the ASR performance for
adults or older children with young children sinceyoung children have evolving speech production
and language skills. Augmenting scripted older
children speech, and both scripted and spontaneous
speech of kindergarten children did not aid the
ASR performance for preschool children. However,naturalistic conversations between young children
and adults help to strengthen the language modelslightly. A major challenge is transcribing speechof young children, due to speech intelligibility, thus
requiring more time and subjective judgement fortranscribers to comprehend such speech in noisy
settings. Often, the transcribers have to rely on
their best guess. Our investigation shows that although high WERs of ≈ 40% occur, to develop
robust ASR models for educational applications ofpreschool children, more similar naturalistic data ofyounger children in such scenarios is needed. Our
future work will emphasize on collection of similardata, focus on strengthening the ASR model, andalso merging location information with ASR output
for feedback to teachers.

Acknowledgements
Work supported by the National Science Foundation Grant #1918032 award to Hansen.

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