MATLAB Programming for Speech Recognition Using HMM

Hidden Markov Models (HMM) represent a fundamental MATLAB programming technique extensively utilized in speech recognition applications. This algorithm operates on statistical models where hidden states generate observable acoustic features, enabling systematic analysis and classification of speech signals. HMM finds broad implementation in speech recognition systems, facilitating both development and optimization processes. Implementing HMM requires specific technical expertise, incorporating key MATLAB functions like hmmtrain for parameter estimation and hmmdecode for sequence analysis. The implementation typically involves feature extraction using MFCC (Mel-Frequency Cepstral Coefficients) followed by Baum-Welch algorithm training and Viterbi path decoding. For speech recognition enthusiasts, mastering HMM programming proves highly valuable, enabling diverse applications including speech-to-text conversion, voice synthesis, and transcription systems. Consequently, proficiency in HMM programming techniques becomes essential for professionals advancing in speech recognition research and development.