Jianrong He
ABSTRACT
This paper presented our studies of automatic speech recognition based on myoelectric signal in the application of Chinese pronounce recognition. Facial myoelectric signal records the articulatory apparatus and thus allows us to recognize spoken words even in silence. In this way, communication is not prone to ambient noise and can be used for patients with language difficulties. Activity sections were segment from original data by moving average method combined with a threshold comparison. According to MES characteristic, the coefficients of time domain and frequency domain, wavelet energy and Mel-frequency cepstral coefficients are selected from the original data for speech recognition. The principal component analysis (PCA) method was applied to reduce dimension and generate a 24-dimensional feature vector. We examined different classifiers with optimized parameters and found that the support vector machine classifier performs the best among all. Our final system achieved a 90.04% accuracy rate on a 10 Chinese digit words task. Therefore, this approach can bring muscle speech recognition within a powerful potential in the application of silent speech.
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Index Terms
- Unvoiced Speech Recognition Algorithm Based on Myoelectric Signal
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