Endpoint Detection and Isolated Word Recognition Using DTW and HMM with Continuous Speech Recognition

Endpoint detection serves as a critical task in speech signal processing, where it identifies the start and end points of speech signals. In speech recognition applications, we frequently need to perform both isolated word recognition and continuous speech recognition. Isolated word recognition involves identifying individual, distinct words from a given speech signal, while continuous speech recognition refers to extracting continuous sentences or text from ongoing speech streams. To accomplish these tasks, we can employ methods based on DTW (Dynamic Time Warping) and HMM (Hidden Markov Models). The DTW algorithm calculates similarity between speech signals by dynamically aligning temporal sequences, commonly implemented using dynamic programming with functions like dtw() in MATLAB that minimize cumulative distance matrices. HMM methods model the probability distributions of speech signals through state transitions and observation probabilities, typically implemented using forward-backward algorithms and Baum-Welch training for parameter estimation. By integrating these approaches—using DTW for template matching in isolated recognition and HMM for sequence modeling in continuous recognition—we can develop accurate and efficient speech recognition systems with robust temporal alignment and statistical modeling capabilities.