Speech Recognition of Isolated Words Using HMM Model

In this text, we can implement speech recognition tasks using the MATLAB programming language. Specifically, we can employ Hidden Markov Models (HMM) for isolated word recognition. HMM is a statistical model particularly suitable for modeling speech signals and performing recognition tasks. Through HMM implementation, we can analyze features within speech signals and match them against predefined isolated words to achieve accurate speech recognition. Using MATLAB, we can develop corresponding code to implement this task through several key steps: loading speech datasets using functions like `audioread()` or `wavread()`, extracting features such as MFCC (Mel-Frequency Cepstral Coefficients) using built-in signal processing toolboxes, training HMM models with algorithms like Baum-Welch for parameter estimation, and performing recognition using Viterbi algorithm for decoding optimal state sequences. The implementation typically involves creating separate HMM models for each vocabulary word, where each model is trained using multiple utterances of the same word. During recognition, the system calculates the likelihood of the input speech signal against each trained HMM and selects the model with the highest probability score. Through this approach, we can effectively utilize MATLAB's computational capabilities and HMM's statistical modeling strengths to accomplish isolated word speech recognition tasks with practical code implementation and algorithmic precision.