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% 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.
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Quantifying the role of vocabulary knowledge in predicting future word learning
Can we predict the words a child is going to learn next given information about the words that a child knows now? Do different representations of a child’s vocabulary knowledge affect our ability to predict the acquisition of lexical items for individual children? Past research has often focused on population statistics of vocabulary growth rather than prediction of words an individual child is likely to learn next. We consider a neural network approach to predict vocabulary acquisition. Specifically, we investigate how best to represent the child’s current vocabulary in order to accurately predict future learning. The models we consider are based on qualitatively different sources of information: descriptive information about the child, the specific words a child knows, and representations that aim to capture the child’s aggregate lexical knowledge. Using longitudinal vocabulary data from children aged 15-36 months, we construct neural network models to predict which words are likely to be learned by a particular child in the coming month. Many models based on child-specific vocabulary information outperform models with child information only, suggesting that the words a child knows influence prediction of future language learning. These models provide an understanding of the role of current vocabulary knowledge on future lexical growth.
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- Award ID(s):
- 1631428
- NSF-PAR ID:
- 10113806
- Date Published:
- Journal Name:
- IEEE transactions on cognitive and developmental systems
- ISSN:
- 2379-8920
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TCDS.2019.2928023
