MATLAB Implementation of Speech Preprocessing for Speech Recognition

In the speech recognition preprocessing pipeline, a series of steps including speech signal preprocessing and feature parameter extraction are performed. Initially, speech signals undergo preprocessing operations such as noise removal, denoising, and signal normalization. These can be implemented in MATLAB using functions like `medfilt1()` for median filtering, `wiener2()` for Wiener filtering-based denoising, and custom normalization functions to scale amplitude values between -1 and 1. Subsequently, feature parameters are extracted from the preprocessed speech signals using techniques like MFCC (Mel Frequency Cepstral Coefficients). The MATLAB implementation typically involves several key stages: framing the signal using `buffer()` function, applying Hamming window with `hamming()` function, computing power spectrum via `fft()`, mapping to Mel-scale filterbanks using triangular filters, and finally applying Discrete Cosine Transform (DCT) with `dct()` function to obtain the cepstral coefficients. The entire MFCC extraction process can be efficiently implemented using MATLAB's Audio Toolbox functions like `mfcc()` or through custom algorithm implementation. These preprocessing steps and feature extraction procedures are crucial for enhancing the accuracy and performance of speech recognition systems. Proper implementation ensures robust feature vectors that effectively capture speech characteristics while minimizing environmental noise interference. The MATLAB code typically includes parameter optimization for frame size (commonly 20-40ms), overlap percentage (typically 50-75%), and the number of MFCC coefficients (usually 12-20 including the 0th coefficient).