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1. Fearless Steps APOLLO: Challenges in keyword spotting and topic detection for naturalistic audio streams

   Joglekar, A.; Lopez-Espejo, I.; Hansen, J.H.L. (May 2023, Program of the meeting Acoustical Society of America)

   Fearless Steps (FS) APOLLO is a + 50,000 hr audio resource established by CRSS-UTDallas capturing all communications between NASA-MCC personnel, backroom staff, and Astronauts across manned Apollo Missions. Such a massive audio resource without metadata/unlabeled corpus provides limited benefit for communities outside Speech-and-Language Technology (SLT). Supplementing this audio with rich metadata developed using robust automated mechanisms to transcribe and highlight naturalistic communications can facilitate open research opportunities for SLT, speech sciences, education, and historical archival communities. In this study, we focus on customizing keyword spotting (KWS) and topic detection systems as an initial step towards conversational understanding. Extensive research in automatic speech recognition (ASR), speech activity, and speaker diarization using manually transcribed 125 h FS Challenge corpus has demonstrated the need for robust domain-specific model development. A major challenge in training KWS systems and topic detection models is the availability of word-level annotations. Forced alignment schemes evaluated using state-of-the-art ASR show significant degradation in segmentation performance. This study explores challenges in extracting accurate keyword segments using existing sentence-level transcriptions and proposes domain-specific KWS-based solutions to detect conversational topics in audio streams. 

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2. Fearless steps Apollo: Advancements in robust speech technologies and naturalistic corpus development from Earth to the Moon

   Hansen, J.H.L.; Joglekar, A.; Chandra-Shekar, M. (December 2022, Acoustical Society of America (Fall Meeting))

   Recent developments in deep learning strategies have revolutionized Speech and Language Technologies(SLT). Deep learning models often rely on massive naturalistic datasets to produce the necessary complexity required for generating superior performance. However, most massive SLT datasets are not publicly available, limiting the potential for academic research. Through this work, we showcase the CRSS-UTDallas led efforts to recover, digitize, and openly distribute over 50,000 hrs of speech data recorded during the 12 NASA Apollo manned missions, and outline our continuing efforts to digitize and create meta-data through diarization of the remaining 100,000hrs. We present novel deep learning-based speech processing solutions developed to extract high-level information from this massive dataset. Fearless-Steps APOLLO resource is a 50,000 hrs audio collection from 30-track analog tapes originally used to document Apollo missions 1,7,8,10,11,&13. A customized tape read-head developed to digitize all 30 channels simultaneously has been deployed to expedite digitization of remaining mission tapes. Diarized transcripts for these unlabeled audio communications have also been generated to facilitate open research from speech sciences, historical archives, education, and speech technology communities. Robust technologies developed to generate human-readable transcripts include: (i) speaker diarization, (ii) speaker tracking, and (iii) text output from speech recognition systems. 

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3. 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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4. 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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5. 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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