Speech Recognition System Based on Visual Feature for the Hearing Impaired

The movements of talkers´ face, nose, mouth and throat are known to convey visual cues and represent several different kinds of informationl, and that can improve speech recognition rate, especially for persons with speech-impairments. We proposed a new speech recognition method using these visual features and hidden Markov model (HMM). Based on global optimisation, a new genetic algorithm (GA) for training HMM was proposed. Six chinese vowels were taken as the experimental data, ten handicapped speakers were taken as the testee. Recognition experiments show that the method is effective and high speed and accuracy for speech recognition. At present, the average recognition rate is 91.47% using improved HMM and 88.96% using the classic training HMM algorithm, So the features has very good robustness and the improved HMM is very good.