Efficient speech recognition system for hearing impaired children in classical Tamil language

Abstract

This paper presents the development of the robust speech recognition system for the children with hearing impairment. It is a challenging task to recognise the distorted speeches of the hearing impaired since the characteristics of the speeches uttered by these people normally have variations in terms of accent, pronunciation and speed. Because of their inability to hear, they are not able to speak even though their nasal and oral cavities aiding for the speech production are perfect like normal persons. This work mainly emphasises the use of MFCC & MF-PLP features at the front end and HMM & K-means clustering at the back end. Performance of the system is evaluated and compared for the two modelling techniques and recognition accuracy is 94%, 97% and 84% for MFCC with HMM and accuracy is 98.3%, 93.5% and 93.6% for MF-PLP with K-means clustering for recognition system developed for recognising isolated digits, connected words and continuous speeches of hearing impaired. Noteworthy point to be mentioned is that, though the clustering technique is an old technique, it is proved that it gives better results as compared to HMM.