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1. Measuring Frequency of Child-directed WH-Question Words for Alternate Preschool Locations using Speech Recognition and Location Tracking Technologies

   https://doi.org/10.1145/3461615.3485440  

   Kothalkar, P. ; Datla, S. ; Dutta, S. ; Hansen, J.H.L. ; Seven, Y. ; Irvin, D. ; Buzhardt J. ( October 2021 , ACM ICMI - WoCBU-2021: Workshop on Bridging Social Sciences and AI for Understanding Child Behavior)

   Speech and language development in children are crucial for ensuring effective skills in their long-term learning ability. A child’s vocabulary size at the time of entry into kindergarten is an early indicator of their learning ability to read and potential long-term success in school. The preschool classroom is thus a promising venue for assessing growth in young children by measuring their interactions with teachers as well as classmates. However, to date limited studies have explored such naturalistic audio communications. Automatic Speech Recognition (ASR) technologies provide an opportunity for ’Early Childhood’ researchers to obtain knowledge through automatic analysis of naturalistic classroom recordings in measuring such interactions. For this purpose, 208 hours of audio recordings across 48 daylong sessions are collected in a childcare learning center in the United States using Language Environment Analysis (LENA) devices worn by the preschool children. Approximately 29 hours of adult speech and 26 hours of child speech is segmented using manual transcriptions provided by CRSS transcription team. Traditional as well as End-to-End ASR models are trained on adult/child speech data subset. Factorized Time Delay Neural Network provides a best Word-Error-Rate (WER) of 35.05% on the adult subset of the test set. End-to-End transformer models achieve 63.5%more »WER on the child subset of the test data. Next, bar plots demonstrating the frequency of WH-question words in Science vs. Reading activity areas of the preschool are presented for sessions in the test set. It is suggested that learning spaces could be configured to encourage greater adult-child conversational engagement given such speech/audio assessment strategies.« less
2. End-to-end Child-Adult Speech Diarization in naturalistic conditions of preschool classrooms using room-independent ResNet model

   Kothalkar, P.V. ; Buzhardt, J. ; Irvin, D. ; Hansen, J.H.L. ( August 2022 , The journal of the Acoustical Society of America)

   Speech and language development are early indicators of overall analytical and learning ability in children. The preschool classroom is a rich language environment for monitoring and ensuring growth in young children by measuring their vocal interactions with teachers and classmates. Early childhood researchers are naturally interested in analyzing naturalistic vs. controlled lab recordings to measure both quality and quantity of such interactions. Unfortunately, present-day speech technologies are not capable of addressing the wide dynamic scenario of early childhood classroom settings. Due to the diversity of acoustic events/conditions in such daylong audio streams, automated speaker diarization technology would need to be advanced to address this challenging domain for segmenting audio as well as information extraction. This study investigates an alternate Deep Learning-based diarization solution for segmenting classroom interactions of 3-5 year old children with teachers. In this context, the focus on speech-type diarization which classifies speech segments as being either from adults or children partitioned across multiple classrooms. Our proposed ResNet model achieves a best F1-score of ∼71.0% on data from two classrooms, based on dev and test sets of each classroom. Additionally, F1-scores are obtained for individual segments with corresponding speaker tags (e.g., adult vs. child), which provide knowledge formore »educators on child engagement through naturalistic communications. The study demonstrates the prospects of addressing educational assessment needs through communication audio stream analysis, while maintaining both security and privacy of all children and adults. The resulting child communication metrics have been used for broad-based feedback for teachers with the help of visualizations.« less
3. Tagging child-adult interactions in naturalistic, noisy, daylong school environments using i-vector based diarization system

   https://doi.org/10.21437/SLaTE.2019-17  

   Kothalkar, Prasanna V. ; Irvin, Dwight ; Luo, Ying ; Rojas, Joanne ; Nash, John ; Rous, Beth ; Hansen, John H. ( August 2020 , ISCA SLaTE-2019 Workshop)

   Assessing child growth in terms of speech and language is a crucial indicator of long term learning ability and life-long progress. Since the preschool classroom provides a potent opportunity for monitoring growth in young children’s interactions, analyzing such data has come into prominence for early childhood researchers. The foremost task of any analysis of such naturalistic recordings would involve parsing and tagging the interactions between adults and young children. An automated tagging system will provide child interaction metrics and would be important for any further processing. This study investigates the language environment of 3-5 year old children using a CRSS based diarization strategy employing an i-vector-based baseline that captures adult-to-child or childto- child rapid conversational turns in a naturalistic noisy early childhood setting. We provide analysis of various loss functions and learning algorithms using Deep Neural Networks to separate child speech from adult speech. Performance is measured in terms of diarization error rate, Jaccard error rate and shows good results for tagging adult vs children’s speech. Distinction between primary and secondary child would be useful for monitoring a given child and analysis is provided for the same. Our diarization system provides insights into the direction for preprocessing and analyzing challengingmore »naturalistic daylong child speech recordings.« less
4. Challenges remain in Building ASR for Spontaneous Preschool Children Speech in Naturalistic Educational Environments

   https://doi.org/10.21437/Interspeech.2022-555  

   Dutta, Satwik ; Tao, Sarah Anne ; Reyna, Jacob C. ; Hacker, Rebecca Elizabeth ; Irvin, Dwight W. ; Buzhardt, Jay F. ; Hansen, John H.L. ( September 2022 , ISCA INTERSPEECH-2022)

   Monitoring child development in terms of speech/language skills has a long-term impact on their overall growth. As student diversity continues to expand in US classrooms, there is a growing need to benchmark social-communication engagement, both from a teacher-student perspective, as well as student-student content. Given various challenges with direct observation, deploying speech technology will assist in extracting meaningful information for teachers. These will help teachers to identify and respond to students in need, immediately impacting their early learning and interest. This study takes a deep dive into exploring various hybrid ASR solutions for low-resource spontaneous preschool (3-5yrs) children (with & without developmental delays) speech, being involved in various activities, and interacting with teachers and peers in naturalistic classrooms. Various out-of-domain corpora over a wide and limited age range, both scripted and spontaneous were considered. Acoustic models based on factorized TDNNs infused with Attention, and both N-gram and RNN language models were considered. Results indicate that young children have significantly different/ developing articulation skills as compared to older children. Out-of-domain transcripts of interactions between young children and adults however enhance language model performance. Overall transcription of such data, including various non-linguistic markers, poses additional challenges.
5. Speech and language processing for assessing child–adult interaction based on diarization and location

   John H. L. Hansen, Maryam Najafian ( January 2019 , International journal of speech technology)

   Understanding and assessing child verbal communication patterns is critical in facilitating effective language development. Typically speaker diarization is performed to explore children’s verbal engagement. Understanding which activity areas stimulate verbal communication can help promote more efficient language development. In this study, we present a two stage children vocal engagement prediction system that consists of (1) a near to real-time, noise robust system that measures the duration of child-to-adult and child-to-child conversations, and tracks the number of conversational turn-takings, (2) a novel child location tracking strategy, that determines in which activity areas a child spends most/least of their time. A proposed child–adult turn-taking solution relies exclusively on vocal cues observed during the interaction between a child and other children, and/or classroom teachers. By employing a threshold optimized speech activity detection using a linear combination of voicing measures, it is possible to achieve effective speech/non-speech segment detection prior to conversion assessment. This TO-COMBO-SAD reduces classification error rates for adult-child audio by 21.34% and 27.3% compared to a baseline i-Vector and standard Bayesian Information Criterion diarization systems, respectively. In addition, this study presents a unique location tracking system adult-child that helps determine the quantity of child–adult communication in specific activity areas, and whichmore »activities stimulate voice communication engagement in a child–adult education space. We observe that our proposed location tracking solution offers unique opportunities to assess speech and language interaction for children, and quantify the location context which would contribute to improve verbal communication.« less