Comparative Experiments to Evaluate the Use of Syllables for the Improvement of Automatic Recognition of Dysarthric Speech

In this paper, we propose to use syllables as the acoustic units representing speech signals in an automatic speech recognition (ASR) system in order to improve the performance of the automatic recognition of dysarthric speech. The motivation behind using syllables comes from studies of human perception which demonstrate the central role of the syllable played in human perception and generation of speech. To test our proposed approach, a syllable-based speaker-independent HMM-based ASR system was designed using Hidden Markov Model Toolkit (HTK). A series of experiments on dysarthric speech has been carried out using a subset of NEMOURS database. The obtained results show that the relative improvement in the recognition rate using syllables were found to be 8.18% and 15.48% compared to the recognition rates obtained using monophones and triphones, respectively.