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A HMM Integrated SVM Model for Hindi Speech Recognition
Speech is one of the most useful and effective communication medium used as the biometric feature of human being. Speech Recognition is considered as important part of various applications for biometric recognition and translation. Speech can be acquired inexpensively using some mic or phone device. But because of different noise factors that can be included during acquisition increases the complexity in recognition process. Because of this, there is the requirement of more effective and reliable approach for speech recognition. In this present work, a statistical analysis based predictive model is defined to improve the speech recognition. The presented work is defined in the form of a layered model. In first layer of this model, the speech signal improvement is done. To achieve this, the Discrete Wavelet Transform (DWT) and spectral subtraction based approach is defined for noise reduction. This layer improves the speech features. In second layer, the Hidden Markov Model (HMM) improved statistical model is defined to generate the speech features. This layer uses the segmented mean value, standard deviation, variation analysis as the statistical features. In final stage, Distance based mapping is applied on all these features collectively to perform the recognition. The work is implemented for Hindi word recognition. The work is implemented in Matlab environment.
Discrete Wavelet Transform (DWT), Hidden Markov Model (HMM), Speech Recognition, Spectral Subtraction, Support Vector Machine (SVM).
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