LPC Analysis of Speech Signals

Implementing LPC analysis of speech signals using MATLAB represents a classic and widely adopted methodology. This approach performs speech feature extraction through signal analysis and modeling, making it applicable to speech recognition, speech synthesis, and related domains. In LPC analysis, linear predictive modeling of speech signals enables estimation of vocal tract characteristics and formant information. The core implementation typically involves using MATLAB's `lpc()` function, which calculates linear prediction coefficients by solving the autocorrelation equations through Levinson-Durbin recursion. Key algorithmic steps include framing the speech signal into short segments, applying windowing functions like Hamming window to reduce spectral leakage, and computing autocorrelation coefficients before the LPC computation. This analytical method holds significant application value in speech processing领域, contributing substantially to research on speech signal characteristics and the advancement of speech processing algorithms. The technique's effectiveness stems from its ability to model the speech production mechanism by representing current speech samples as linear combinations of previous samples, with typical implementation using 10-16 prediction coefficients for optimal balance between accuracy and computational efficiency.