Collaborative Research: RI: Small: From Ultrasound and MRI to articulatory and acoustic models of child speech development

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Scientific models of adult speech matured several decades ago and have proven extremely useful in speech technologies. In contrast, models of child speech remain poorly motivated and grounded in adult rather than child data, in part because of the difficulties involved in collecting speech articulation data from children. The lack of appropriate models for child speech is increasingly problematic as technological application areas for children emerge and become more important. The project will advance the scientific and technological state-of-the-art related to child speech to develop a variety of applications such as speech-based interactive systems for literacy development and learning.Scientifically, the project will 1) reveal relationships between articulation and acoustics in child speech, 2) characterize previously unobserved processes of speech development in children, including anatomical development of the vocal tract, and 3) develop acoustic models and speech technologies for child speech, which can be scalable with age. The different aspects of the project are synergistic: findings from articulation and acoustic experiments will inform the development of algorithms essential to speech technologies for children. Key strengths of the project are the 1) uniqueness of the data corpus that will be created and leveraged, and 2) interdisciplinary approach to studying speech production and developing models of child speech for use in speech technologies. Production data will include 3D/4D ultrasound recordings of the tongue, MRI anatomical scans of the head and neck, palate impressions, and microphone recordings. The corpus will, for the first time, allow the relationship between articulatory and acoustic variability, and the longitudinal development of speech articulation, to be directly explored and modeled for child speech. The project will advance knowledge of articulatory-acoustic relations and their development in children's speech, and their relationship to machine recognition of children's speech.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.