Extraction of LPCC Features

LPCC (Linear Predictive Cepstral Coefficients) is a widely used feature in speech signal processing, extensively applied in speech recognition and speaker recognition domains. It is derived from Linear Predictive Analysis (LPC) cepstral coefficients and effectively characterizes the spectral envelope properties of speech signals.

### Fundamental Principles of LPCC Feature Extraction The core concept of LPCC feature extraction involves modeling speech signals through linear predictive analysis and converting them into cepstral domain coefficients. The key steps include: Preprocessing: Initially, pre-emphasis, framing, and windowing are applied to the speech signal to minimize high-frequency attenuation and spectral leakage effects. LPC Analysis: For each signal frame, linear predictive analysis is performed to compute LPC coefficients, which represent the prediction filter coefficients. LPC to LPCC Conversion: Through recursive formulas, LPC coefficients are transformed into LPCC coefficients to better characterize spectral envelope properties.

### MATLAB Implementation Approach Implementing LPCC feature extraction in MATLAB typically involves the following steps: Speech Signal Reading and Preprocessing: Use the `audioread` function to load the speech signal and apply pre-emphasis (commonly using a first-order high-pass filter). Framing and Windowing: Segment the speech signal into short-term frames (e.g., 20-30ms per frame) and apply Hamming or Hanning windows to reduce edge effects. LPC Coefficient Calculation: Utilize MATLAB's `lpc` function to compute LPC coefficients for each frame; this function employs autocorrelation or covariance methods to solve the linear prediction model. LPCC Coefficient Conversion: Convert LPC coefficients to LPCC coefficients using recursive relations, typically employing recurrence formulas to calculate cepstral coefficients.

### Extended Applications LPCC features are often combined with MFCC (Mel-Frequency Cepstral Coefficients) in speech recognition systems to enhance recognition accuracy. Since LPCC is directly based on the LPC model, it possesses strong modeling capabilities for formant structures in speech, making it suitable for specific speech analysis tasks in certain scenarios.