Speech-Input Speech-Output Communication for Dysarthric Speakers Using HMM-Based Speech Recognition and Adaptive Synthesis System

Abstract

Dysarthria is a motor speech disorder that causes inability to control and coordinate one or more articulators. This makes it difficult for a dysarthric speaker to utter certain speech sound units, thereby producing poorly articulated, slurred, and unintelligible speech. Hence, a speech supportive system needs to be developed to support them in their social difficulties. The current work aims at developing a speech supportive system, the objectives of which are threefold, namely (i) identifying the articulatory errors of each dysarthric speaker, (ii) developing a speech recognition system that corrects the errors in dysarthric speech by incorporating the findings from the first fold using a speaker-specific dictionary and (iii) developing an HMM-based speaker-adaptive speech synthesis system that synthesizes the error-corrected text for each dysarthric speaker retaining their identity. In the current work, the articulatory errors are analysed and identified, for 10 dysarthric speakers from the Nemours dysarthric speech corpus, using isolated-style phoneme recognition system trained with TIMIT speech corpus, followed by product of likelihood Gaussian-based analysis. The estimated articulatory errors are incorporated into a phoneme recognition system using speaker-specific dictionary and bigram language model. The error-corrected text is then synthesized as speech. The synthesized speech is evaluated to check its intelligibility and naturalness using mean opinion score. To further improve the intelligibility, speech rate of the synthesized speech is modified using time-domain pitch synchronous overlap add (TDPSOLA) technique. The results are quite encouraging, and this system is expected to be developed as a speech assistive device for a large vocabulary, in the near future, in a hand-held device.