Linear Predictive Coding (LPC) for Speech Signal Processing

In speech signal processing, Linear Predictive Coding (LPC) stands as a highly efficient analytical technique. By analyzing LPC spectra and LPCC spectra alongside LPC detection methods, it enables accurate determination of pitch periods and formants in speech signals. Implementation in MATLAB proves straightforward yet effective, typically involving steps like frame segmentation, autocorrelation calculation using xcorr(), and solving Levinson-Durbin recursion for LPC coefficients. Beyond analysis, LPC facilitates speech synthesis through parameter manipulation - adjusting LPC coefficients and excitation signals allows generation of diverse speech waveforms. This approach finds extensive application in text-to-speech systems, where functions like filter() reconstruct speech from LPC parameters. Additionally, LPC serves as a fundamental feature extraction method in speech recognition systems; extracting LPC cepstral coefficients (LPCC) creates compact representations suitable for pattern classification algorithms like HMM or neural networks. In summary, Linear Predictive Coding (LPC) represents a versatile and powerful methodology in speech signal processing, with broad applications spanning from speech analysis and synthesis to recognition systems, consistently delivering robust performance across diverse implementations.