hidden markove modeling for text dependent speaker recognition

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hidden markove modeling for text dependent speaker recognition

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Hidden Markov Modeling (HMM) is a powerful statistical approach widely used in text-dependent speaker recognition systems. This technique models speech signals as sequences of observed features (like Mel-Frequency Cepstral Coefficients) generated by hidden states representing phonetic units.

For text-dependent scenarios, HMMs are trained on specific spoken phrases, where both the speaker's voice characteristics and the lexical content are constrained. The system typically follows these steps:

Feature Extraction - Convert raw speech signals into discriminative acoustic features (e.g., MFCCs with delta coefficients). Model Training - Build speaker-specific HMMs for enrolled users, often using Baum-Welch algorithm to optimize state transitions and emission probabilities. Verification Phase - Compare input utterance’s likelihood against both the claimed speaker’s model and a universal background model (UBM) for decision-making.

Key advantages include HMM's ability to handle temporal variability in speech and its robustness to minor pronunciation differences. Challenges involve sensitivity to noisy environments and the need for sufficient enrollment data. Modern systems often combine HMMs with Gaussian Mixture Models (GMM-HMM) or deep learning hybrids for enhanced performance.