MATLAB Code Implementation for Speech Recognition

This article presents a speech recognition program developed using MATLAB. The implementation includes core functionalities such as voice activity detection (VAD) using energy thresholding and zero-crossing rate analysis, and advanced filtering techniques employing Butterworth or Chebyshev filters for noise reduction. The program processes audio signals through multiple stages: preprocessing with framing and windowing (typically Hamming window), feature extraction using Mel-Frequency Cepstral Coefficients (MFCC) or Linear Predictive Coding (LPC), and pattern matching through Dynamic Time Warping (DTW) or Hidden Markov Models (HMM). Key MATLAB functions involved include audioread for signal acquisition, filter design tools for digital filtering, and signal processing toolbox functions for spectral analysis. Additional advanced features optimize recognition accuracy through machine learning algorithms like SVM or neural networks, enhance processing speed via vectorized operations and parallel computing capabilities, and support multilingual adaptation through acoustic model customization. This MATLAB-based solution demonstrates significant potential for various speech processing applications, including voice-controlled systems and biometric authentication.