Speech system for a voice-impaired person

Abstract

This thesis attempts to develop a speaker-dependent speech system for voice impaired people. The system recognizes isolated utterances from a limited vocabulary, and is small and cost-efficient enough to be incorporated into a hand-held system. A 20-dimensional feature vector was generated based on zero crossing and energy content measurements of the speech waveforms. The generated feature vectors were used to train a neural network and the trained network was tested with known and unknown utterances. The system was implemented on an IBM Personal Computer and achieved a recognition rate of 76% on a ten-word database of 16 speakers (8 male and 8 female). A test database, which mimics a voice-impaired person's speech, was developed, and a recognition rate of 60% was observed. The system recognized utterances at an average rate of 0.15 seconds/recognition.