skip to main content

Title: Speech and language processing for assessing child–adult interaction based on diarization and location
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 which activities more » 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
Award ID(s):
Publication Date:
Journal Name:
International journal of speech technology
Sponsoring Org:
National Science Foundation
More Like this
  1. 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
  2. Young children’s friendships fuel essential developmental outcomes (e.g., social-emotional competence) and are thought to provide even greater benefits to children with or at-risk for disabilities. Teacher and parent report and sociometric measures are commonly used to measure friendships, and ecobehavioral assessment has been used to capture its features on a momentary basis. In this proof-of-concept study, we use Ubisense, the Language ENvironmental Analysis (LENA) recorder, and advanced speech processing algorithms to capture features of friendship –child-peer speech and proximity within activity areas . We collected 12,332 1-second speech and location data points. Our preliminary results indicate the focal child at-risk for a disability and each playmate spent time vocalizing near one another across 4 activity areas. Additionally, compared to the Blocks activity area, the children had significantly lower odds of talking while in proximity during Manipulatives and Science. This suggests that the activity areas children occupy may affect their engagement with peers and, in turn, the friendships they development. The proposed approach is a groundbreaking advance to understanding and supporting children’s friendships.
  3. 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
  4. Speech and language development in children is crucial for ensuring optimal outcomes in their long term development and life-long educational journey. A child’s vocabulary size at the time of kindergarten entry is an early indicator of learning to read and potential long-term success in school. The preschool classroom is thus a promising venue for monitoring growth in young children by measuring their interactions with teachers and classmates. Automatic Speech Recognition (ASR) technologies provide the ability for ‘Early Childhood’ researchers for automatically analyzing naturalistic recordings in these settings. For this purpose, data are collected in a high-quality childcare center in the United States using Language Environment Analysis (LENA) devices worn by the preschool children. A preliminary task for ASR of daylong audio recordings would involve diarization, i.e., segmenting speech into smaller parts for identifying ‘who spoke when.’ This study investigates a Deep Learning-based diarization system for classroom interactions of 3-5-year-old children. However, the focus is on ’speaker group’ diarization, which includes classifying speech segments as being from adults or children from across multiple classrooms. SincNet based diarization systems achieve utterance level Diarization Error Rate of 19.1%. Utterance level speaker group confusion matrices also show promising, balanced results. These diarization systems havemore »potential applications in developing metrics for adult-to-child or child-to-child rapid conversational turns in a naturalistic noisy early childhood setting. Such technical advancements will also help teachers better and more efficiently quantify and understand their interactions with children, make changes as needed, and monitor the impact of those changes.« less
  5. 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)