Continuous Speech Recognition Using HMM and MATLAB Implementation
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This article explores HMM-based continuous speech recognition technology and demonstrates implementation using Markov MVoiceRecognia's MATLAB source code. In continuous speech recognition systems, speech signals are first converted into digital waveforms, which are then processed through algorithmic pipelines to generate recognition results. The Hidden Markov Model (HMM) serves as a fundamental statistical framework for modeling temporal patterns in speech data. The provided MATLAB source code enables developers to rapidly prototype recognition systems through key functions including feature extraction (MFCC computation), HMM training (Baum-Welch algorithm), and decoding processes (Viterbi algorithm). Successful implementation requires understanding core principles such as signal preprocessing techniques, state transition probabilities, and emission distributions to advance research and development in speech recognition applications.
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