GMM-Based Speaker Recognition System
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Resource Overview
GMM-based speaker recognition system achieving over 90% identification accuracy with optimized feature extraction and model training
Detailed Documentation
The GMM-based speaker recognition system demonstrates outstanding performance in identification effectiveness, achieving an accuracy rate exceeding 90%. The system employs Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction and utilizes Gaussian Mixture Models (GMM) for speaker modeling. Key implementation components include feature normalization, expectation-maximization (EM) algorithm for GMM parameter estimation, and maximum likelihood classification for speaker identification. The high accuracy is achieved through proper model selection (typically 16-64 Gaussian components) and robust feature preprocessing techniques including voice activity detection and cepstral mean normalization.
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