Mandarin Digit Speech Recognition Based On HMM And ANN Model

Speech recognition has received more and more attention recently due to the important theoretical meaning and practical value. Up to now, most speech recognition is based on conventional linear system theory, such as Hidden Markov Model (HMM) and Dynamic Time Warping (DTW). Wifh the deep study of speech recognition, nonlinear system theory method must be introduced to it. Recently, with the development of nonlinear-system theories such as artificial neural networks (ANN), chaos and fractal, it is possible to apply these theories to speech recognition. Therefore, the research in this paper is oriented on the theory and application of the mixed model HMM-ANN, and the related algorithms and model are developed.Mandarin digital speech recognition technology and implement approach is studied in this thesis. Computing validation, performance analysis and results assessing are handled to each part of speech recognition process such as preprocessing, feature extraction and recognition algorithms. The performance of speech recognition and application characteristic of HMM and ANN methods used in this paper is compared. The research improvement in this paper is oriented on the theory and application of the mixed model HMM-ANN, which is formed by the combination of the continues hidden markov mode (CDHMM) and the self-organized feature mapping (SOFM), and the related algorithms and model are developed.Then, we simulate training and recognizing HMM model and HMM-ANN model algorithm based on MATLAB6.5 and VC++, and get the simulation main results, estimated system performance from antinoise, recognition rate and error recognition rate for length.At lastpoint out the direction of the research improvement.