Comparative analysis of Dysarthric speech recognition: multiple features and robust templates

Research on recognizing the speeches of normal speakers is generally in practice for numerous years. Nevertheless, a complete system for recognizing the speeches of persons with a speech impairment is still under development. In this work, an isolated digit recognition system is developed to recognize the speeches of speech-impaired people affected with dysarthria. Since the speeches uttered by the dysarthric speakers are exhibiting erratic behavior, developing a robust speech recognition system would become more challenging. Even manual recognition of their speeches would become futile. This work analyzes the use of multiple features and speech enhancement techniques in implementing a cluster-based speech recognition system for dysarthric speakers. Speech enhancement techniques are used to improve speech intelligibility or reduce the distortion level of their speeches. The system is evaluated using Gamma-tone energy (GFE) features with filters calibrated in different non-linear frequency scales, stock well features, modified group delay cepstrum (MGDFC), speech enhancement techniques, and VQ based classifier. Decision level fusion of all features and speech enhancement techniques has yielded a 4% word error rate (WER) for the speaker with 6% speech intelligibility. Experimental evaluation has provided better results than the subjective assessment of the speeches uttered by dysarthric speakers. The system is also evaluated for the dysarthric speaker with 95% speech intelligibility. WER is 0% for all the digits for the decision level fusion of speech enhancement techniques and GFE features. This system can be utilized as an assistive tool by caretakers of people affected with dysarthria.