DHB/Collaborative Research: Using Machine Learning to Model the Interplay of Production Dynamics and Perception Dynamics in Phonological Acquision

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This project adapts acoustic modeling techniques for robust Automatic Speech Recognition to a large multi-language database of adult and child speech recordings. It explores how cognitive representations relevant to speech production and perception in any given speech community come to be internalized by normally developing children.Current models of language acquisition cannot account for fine-grained phonetic differences between speech communities and between younger and older speakers within a community because they are based on research that uses alphabetic transcription as the primary observational tool. Because this project is a collaboration among specialists in speech engineering, linguistics, and speech and hearing science it enables child-language researchers to develop finer-grained measures to begin to answer several key questions about the dynamics of language acquisition, including the following three: How do adult speakers from different speech communities perceive the acoustic patterns in words pronounced by speakers of different ages? How do adult stereotypes about children's productions shape speech addressed to children? How do adults' perceptual responses to children's (mis)articulations shape the course of children's mastery of the community-specific patterns? These questions are a key part of understanding what similarities and differences exist across cultures and what these mean for our understanding of the human capacity for speech evolved. In addition, by linking together advances in linguistic and engineering, the modeling results should provide a basis for developing a new approach to speech technology, with broader impacts in several areas such as assistive technology for the disabled and more democratic access to electronic databases.