Abstract
The development of speech recognition system for recognising the speeches of a reasonable person in various languages usually is in fashion and does not involve challenges to be faced by the researchers. For the past ten years, advances are taking place in analysing and recognising the speeches of the hearing impaired because of the deployment of sophisticated processing methods to study the characteristics of speech production. These technological advancements not only pave the way for developing algorithms for recognising the speeches of healthy persons, also recognising the utterances of the hearing impaired. The study on analysing the oral communication skills of hearing-impaired children has received the attention of the researchers, speech pathologists and audiologists to develop assistive tool/system because inadequacy of such skills dramatically affects the social, educational and career opportunities available to them at large. This paper mainly emphasises the need for the development of a more challenging speaker independent speech recognition system for hearing impaired so that the system can respond to the speech uttered by any HI. In this work, Modified Group Delay Features and Stockwell transform cepstral features are used at the front end and vector quantisation (VQ) and Multivariate Hidden Markov Models (MHMM) at the back end for recognising the speeches uttered by any person with hearing disability. Performance of the system is compared for the three modelling techniques VQ, Fuzzy C Means (FCM) clustering and MHMM for the recognition of isolated digits in Tamil. Recognition accuracy is 89.25% and 79.5% for speaker dependent and independent speech recognition system for the hearing impaired. Performance of the system reveals that this system may be deployed to understand the speeches uttered by any hearing impaired speaker, improve the social status of the people with hearing impairment and mitigate the social stigma in leading a normal life.
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