HMM-neural network monophone models for computer based articulation training for the hearing impaired

A visual speech training aids for persons with hearing impairments has been developed using a Windows-based multimedia computer. In previous papers, the signal processing steps and display options have been described for giving real-time feedback about the quality of pronunciation for 10 steady-state American English monopthong vowels (/aa/, /iy/, /uw/, /ae/, /er/, /ih/, /eh/, /ao/, /ah/, and /uh/). This vowel training aid is thus referred to as a vowel articulation training aid (VATA). In the present paper, methods are described to develop a monophone-based hidden Markov model/neural network recognizer such the real-time visual feedback can be given about the quality of pronunciation of short words and phrases. Experimental results are reported which indicate a high degree of accuracy for labeling and segmenting the CVC database developed for "training" the display.

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