Speech Recognition Using MATLAB with Hidden Markov Model Implementation

In this article, I present the MATLAB source code for an HMM-based speech recognition system. This system serves as a powerful tool for recognizing and analyzing speech signals. Built upon Hidden Markov Model (HMM) principles, it achieves efficient and accurate speech recognition through feature modeling and training of speech signals. The implementation includes key components such as Mel-Frequency Cepstral Coefficients (MFCC) for feature extraction, Baum-Welch algorithm for HMM parameter training, and Viterbi algorithm for decoding the most probable word sequence. MATLAB provides an ideal programming environment with extensive toolboxes and functions that facilitate speech signal processing and algorithm implementation. The code structure demonstrates how to preprocess raw audio data, extract temporal and spectral features, train HMM models for different phonemes or words, and implement recognition logic. Key functions include audio file reading using audioread(), spectral analysis through fft() and frame-based processing, and HMM toolkit functions for state transition probability calculations. By studying this source code, developers can gain deeper insights into HMM speech recognition mechanics and modify or optimize the system according to specific requirements. The modular design allows for customization of feature extraction parameters, HMM topology adjustments, and integration with language models. This enables creation of tailored speech recognition systems for various applications such as voice commands, transcription services, or biometric authentication. In summary, this MATLAB source code for HMM speech recognition provides a valuable resource for achieving advanced results in speech signal processing, offering both educational value and practical implementation foundations for research and development projects.