HMM-Based Speech Recognition System Developed Using MATLAB

This article discusses a speech recognition system developed using MATLAB, which is based on Hidden Markov Models (HMM). HMM is a widely used statistical model for sequential data modeling. In speech recognition applications, HMM establishes mappings between speech signals and corresponding text, enabling automated speech-to-text conversion. The development process involves several key implementation phases: HMM model training using Baum-Welch algorithm, Viterbi algorithm for decoding optimal state sequences, and extensive signal processing operations. The system performs speech signal preprocessing (noise reduction, normalization), framing (typically 20-30ms frames with 10ms overlap), and feature extraction (commonly using MFCC - Mel-Frequency Cepstral Coefficients with 12-13 coefficients plus energy and delta features). Post-processing modules include pinyin-to-Chinese character conversion utilizing language models, and optional speech synthesis for output verification. The implementation requires coordinated use of MATLAB's Signal Processing Toolbox for audio analysis, Statistics and Machine Learning Toolbox for HMM operations, and custom algorithms for language processing. Developing such an HMM-based speech recognition system represents a complex integration of multiple signal processing and machine learning techniques.