Parameter Extraction for LPCC and MFCC in Speaker Recognition

This document explores two fundamental parameter extraction methods in speech signal processing: Linear Predictive Cepstral Coefficients (LPCC) and Mel-Frequency Cepstral Coefficients (MFCC). These parameter extraction techniques serve as critical components in speaker recognition systems. We provide comprehensive code implementations demonstrating how to extract these acoustic features, including detailed explanations of the underlying algorithms and key functions involved in the feature extraction pipeline.

The LPCC implementation involves linear prediction analysis to model the vocal tract characteristics, followed by cepstral transformation. Key steps include frame blocking, windowing, autocorrelation analysis, and Levinson-Durbin recursion for LPC coefficient calculation. The MFCC implementation incorporates psychoacoustic principles through Mel-filterbank processing, featuring pre-emphasis, framming, windowing, FFT, Mel-filtering, logarithmic compression, and discrete cosine transform stages.

Both methods produce feature vectors that effectively capture speaker-specific characteristics while reducing dimensionally for efficient pattern matching in recognition systems.