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A proof of concept system is developed to provide a broad assessment of speech development issues in children. It has been designed to enable non-experts to complete an initial screening of children's speech with the aim of reducing the workload on Speech Language Pathology services. The system was composed of an acoustic model trained by neural networks with split temporal context features and a constrained HMM-encoded with the knowledge of Speech Language Pathologists. Results demonstrated the system was able to improve PER by 33% compared with standard HMM decoders, with a minimum PER of 19.03% achieved. Identification of Phonological Error Patterns with up to 94% accuracy was achieved despite utilizing only a small corpus of disordered speech from Australian children. These results indicate the proposed system is viable and the direction of further development are outlined in the paper.

Item Type:	Refereed Conference Paper
Keywords:	automated speech recognition, phonological error patterns, speech assessment tools, speech therapy
Research Division:	Language, Communication and Culture
Research Group:	Linguistics
Research Field:	Phonetics and speech science
Objective Division:	Health
Objective Group:	Clinical health
Objective Field:	Clinical health not elsewhere classified
UTAS Author:	Ward, L (Ms Lauren Ward)
ID Code:	117732
Year Published:	2016
Web of Science® Times Cited:	9
Deposited By:	Information and Communication Technology
Deposited On:	2017-06-26
Last Modified:	2017-11-23
Downloads:	0