Speaker Recognition by training GMM models

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Speaker Recognition by training GMM models

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Speaker recognition using Gaussian Mixture Models (GMM) is a classic approach in biometric authentication. The core idea involves training unique GMMs for each speaker's voice to capture their distinct vocal characteristics. Here's how it works:

Feature Extraction – First, audio recordings are processed to extract relevant features, typically Mel-Frequency Cepstral Coefficients (MFCCs), which represent the vocal tract's shape and dynamics.

Model Training – Each speaker’s voice data is used to train a GMM. This statistical model assumes that a speaker’s voice features form clusters in the feature space, approximated as weighted combinations of Gaussian distributions.

Recognition & Verification – During testing, a new voice sample is compared against all stored GMMs. The system either identifies the best-matching speaker (identification) or verifies if the sample matches a claimed identity (verification).

Impostor Detection – A threshold-based approach helps detect impostors. If the likelihood score of the test sample is below a certain threshold for all models, it’s flagged as an unauthorized speaker.

GMMs are effective due to their ability to model complex voice distributions with relatively low computational overhead. However, modern deep learning methods (like neural networks) often outperform GMMs in large-scale systems. Still, GMMs remain relevant for lightweight or explainable solutions.