Speaker Recognition Using GMM with MFCC Features

Speaker recognition can be effectively implemented using MFCC (Mel-Frequency Cepstral Coefficients) feature extraction combined with GMM (Gaussian Mixture Model). MFCC feature extraction is a widely adopted audio processing technique that transforms raw audio signals into a set of representative feature vectors by simulating human auditory perception. The implementation typically involves pre-emphasis, framing, windowing, FFT, Mel-filterbank application, and DCT operations to produce 12-13 cepstral coefficients plus energy features.

GMM serves as a powerful statistical model for modeling speaker characteristics by representing feature distributions as a weighted sum of multiple Gaussian components. In practical implementation, the Expectation-Maximization (EM) algorithm is commonly used for GMM parameter estimation, where the model learns the unique vocal patterns of each speaker through training data. The recognition process involves computing likelihood scores using the log-likelihood function to determine the most probable speaker identity.

The integration of these two methodologies enables accurate speaker identification through a systematic pipeline: audio preprocessing → MFCC feature extraction → GMM model training → probability scoring for classification. This approach plays a crucial role in various applications including voice biometrics, speaker verification systems, and secure authentication platforms, with typical Python implementations utilizing libraries like librosa for MFCC extraction and scikit-learn for GMM modeling.