LPCC (Linear Predictive Cepstral Coefficients) for Speech Feature Extraction in MATLAB-based Speech Recognition

This article explores LPCC (Linear Predictive Cepstral Coefficients) as a speech feature extraction method for speech recognition systems implemented in MATLAB. The method extracts significant acoustic information from speech signals, including speaker identity and emotional states. The LPCC implementation involves multiple signal processing stages: pre-emphasis to enhance high-frequency components, frame blocking to divide the signal into short segments, windowing (typically using Hamming windows) to reduce spectral leakage, linear predictive analysis to model vocal tract parameters, and cepstral analysis to decompose the source-filter characteristics. In MATLAB, key functions like lpc() for linear prediction coefficients calculation and custom implementations for cepstral transformation are typically employed. These techniques enable better understanding of speech signal characteristics and provide more accurate input features for speech recognition systems. The article details the step-by-step implementation methodology, discusses applications in speech recognition domains, analyzes limitations such as sensitivity to noise and speaker variability, and suggests potential improvements including integration with MFCC or deep learning approaches.