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1. Teacher-Student learning based Acoustic Models for Robust Speech Recognition in Naturalistic Childhood Classroom settings

   Kothalkar, P.V. (February 2023, Speech communication)

   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 both teachers and classmates. Early childhood researchers recognize the importance in analyzing naturalistic vs. controlled lab recordings to measure both quality and quantity of child interactions. Recently, large language model-based speech technologies have performed well on conversational speech recognition. In this regard, we assess performance of such models on the wide dynamic scenario of early childhood classroom settings. This study investigates an alternate Deep Learning-based Teacher-Student learning solution for recognizing adult speech within preschool interactions. Our proposed adapted model achieves the best F1-score for recognizing most frequent 400 words on test sets for both classrooms. Additionally, F1-scores for alternate word groups provides a breakdown of performance across relevant language-based word-categories. 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 from this study can also be used for broad-based feedback for teachers. 

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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 for 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. 

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3. Child-adult speech diarization in naturalistic conditions of preschool classrooms using room-independent ResNet model and automatic speech recognition-based re-segmentation

   https://doi.org/10.1121/10.0024353  

   Kothalkar, Prasanna V; Hansen, John_H L; Irvin, Dwight; Buzhardt, Jay (February 2024, 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 alternate deep learning-based lightweight, knowledge-distilled, diarization solutions for segmenting classroom interactions of 3–5 years 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 lightest CNN model achieves a best F1-score of ∼76.0% on data from two classrooms, based on dev and test sets of each classroom. It is utilized with automatic speech recognition-based re-segmentation modules to perform child-adult diarization. Additionally, F1-scores are obtained for individual segments with corresponding speaker tags (e.g., adult vs child), which provide knowledge for 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. 

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4. End-to-end Child-Adult Speech Diarization in naturalistic conditions of preschool classrooms using room-independent ResNet model

   Kothalkar, P.V. (May 2023, The Journal of the Acoustical Society of America (submitted))

   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 ∼78.0% on data from two classrooms, based on dev and test sets of each classroom. It is utilized with Automatic Speech Recognition-based resegmentation modules to perform child-adult diarization. Additionally, F1-scores are obtained for individual segments with corresponding speaker tags (e.g., adult vs. child), which provide knowledge for 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. 

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5. Quantifying Engagement in Preschool Classrooms: Conversational Turn-Taking & Topic Initiations

   Tao, S.A.; Dutta, S.; Seven, Y.; Kothalkar, P.; Irvin, D.W.; Buzhardt, J.; Hansen J.H.L. (March 2022, ASEE-GSW–2022: American Soc. of Engineering Education – Gulf-SouthWest Section Conf.)

   Adult-child interaction is an important component for language development in young children. Teachers responsible for the language acquisition of their students have a vested interest in improving such conversation in their classrooms. Advancements in speech technology and natural language processing can be used as an effective tool by teachers in pre-school classrooms to acquire large amounts of conversational data, receive feedback from automated conversational analysis, and amend their teaching methods. Measuring engagement among pre-school children and teachers is a challenging task and not well defined. In this study, we focus on developing criteria to measure conversational turn-taking and topic initiation during adult-child interactions in preschool environments. However, counting conversational turns, conversation initiations, or vocabulary alone is not enough to judge the quality of a conversation and track language acquisition. It is necessary to use a combination of the three and include a measurement of the complexity of vocabulary. The next iterative of this problem is to deploy various solutions from speech and language processing technology to automate these measurements. * (2022 ASEE Best Student Paper Award Winner) 

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