Chinese Speech Recognition System Based on Continuous Density Hidden Markov Models

Implementing a Chinese speech recognition system based on Continuous Density Hidden Markov Models (CD-HMM) in MATLAB is a crucial task in speech processing. This system enables accurate recognition and understanding of Chinese spoken language. The implementation process involves several key stages: first, collecting substantial Chinese speech datasets followed by preprocessing and feature extraction using techniques like Mel-Frequency Cepstral Coefficients (MFCC) extraction through MATLAB's audio processing toolbox. Next, the system employs CD-HMM for building robust speech recognition models, where the implementation includes Baum-Welch algorithm for model training and Viterbi algorithm for decoding recognition results. The model optimization phase involves parameter tuning using MATLAB's statistical tools to enhance accuracy and performance metrics. Furthermore, this system can be integrated with other speech processing technologies and applications, extending its functionality to areas such as speech translation systems and voice-controlled interfaces through MATLAB's API integration capabilities. The Chinese speech recognition system based on CD-HMM demonstrates significant application potential and research value in multilingual speech technology development.