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 »
Assessing Child Communication Engagement via Speech Recognition in Naturalistic Active Learning Spaces
The ability to assess children’s conversational interaction is critical
in determining language and cognitive proficiency for typically
developing and at-risk children. The earlier at-risk child
is identified, the earlier support can be provided to reduce the
social impact of the speech disorder. To date, limited research
has been performed for young child speech recognition in classroom
settings. This study addresses speech recognition research
with naturalistic children’s speech, where age varies from 2.5
to 5 years. Data augmentation is relatively under explored for
child speech. Therefore, we investigate the effectiveness of data
augmentation techniques to improve both language and acoustic
models. We explore alternate text augmentation approaches
using adult data, Web data, and via text generated by recurrent
neural networks. We also compare several acoustic augmentation
techniques: speed perturbation, tempo perturbation, and
adult data. Finally, we comment on child word count rates to
assess child speech development.
- Award ID(s):
- 2016725
- Publication Date:
- NSF-PAR ID:
- 10180044
- Journal Name:
- ISCA ODYSSEY-2020
- Page Range or eLocation-ID:
- 396 to 401
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.21437/Odyssey.2020-56
