MATLAB Implementation of Speaker Recognition with Neural Network Model

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Speaker recognition code implementation featuring endpoint detection, pre-emphasis, MFCC feature extraction, and neural network classification model

Detailed Documentation

In speaker recognition systems, signal processing techniques are typically employed to extract voice features. Key preprocessing steps include endpoint detection, which identifies speech segments from background noise using algorithms like short-term energy and zero-crossing rate, and pre-emphasis, which enhances high-frequency components through a simple FIR filter (e.g., y(n) = x(n) - α*x(n-1) with α ≈ 0.97). Following feature extraction, Mel-Frequency Cepstral Coefficients (MFCCs) serve as the primary feature representation, implemented through frame blocking, windowing, FFT, Mel-filterbank application, and DCT transformation. Finally, to construct an effective speaker recognition model, a neural network classifier is adopted, typically implemented using MATLAB's Deep Learning Toolbox with feedforward or recurrent architectures for mapping MFCC features to speaker identities.

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