MATLAB Code Implementation for Speaker Recognition

In this document, we explore how to develop an effective MATLAB code for speaker recognition. To achieve this objective, we need to understand the fundamentals of speech signal processing and pattern recognition. Specifically, we will learn how to analyze speech signals using Power Spectral Density (PSD) and Linear Predictive Coding (LPC) coefficients, and classify speakers using Gaussian Mixture Models (GMM). The implementation demonstrates how to extract spectral features using MATLAB's signal processing toolbox functions like pwelch for PSD estimation and lpc for linear prediction analysis. For classification, we utilize the statistical toolbox to create GMMs with gmdistribution.fit for modeling speaker characteristics. Additionally, we cover voice activity detection using zero-crossing rate and energy thresholds to preprocess audio signals. The document provides detailed step-by-step explanations and complete MATLAB code examples, enabling readers to follow our methodology and implement their own speaker recognition algorithms. Each section includes practical code snippets showing feature extraction, model training with expectation-maximization algorithm, and real-time recognition testing using MFCC (Mel-Frequency Cepstral Coefficients) as complementary features.