LPCC Speech Recognition: Linear Predictive Cepstral Coefficients

In this article, we explore Linear Predictive Cepstral Coefficients (LPCC) for speech recognition applications. LPCC represents a widely adopted technique in speech processing for extracting discriminant features from audio signals. We examine implementation approaches where LPCC parameters are derived through linear predictive coding (LPC) analysis, typically involving autocorrelation calculation, Levinson-Durbin recursion for LPC coefficient computation, and subsequent conversion to cepstral coefficients using recursive relations. The discussion covers parameter optimization strategies for improving speech recognition accuracy, including frame size adjustment, pre-emphasis filtering, and pole enhancement techniques to accommodate diverse languages and dialects. We conduct comparative analysis between LPCC and alternative speech recognition features like MFCCs, highlighting LPCC's computational efficiency in representing vocal tract characteristics while noting its sensitivity to noise. Finally, we investigate LPCC's extended applications in audio processing domains such as music analysis and voice synthesis systems, where similar coefficient extraction pipelines can be adapted for spectral envelope modeling.