Extraction of RASTA-PLP and PLP Features for Speech Signals

In speech signal processing, RASTA-PLP and PLP features represent two widely adopted methods for extracting acoustic characteristics. RASTA-PLP employs a signal-based averaged autoregressive (AR) modeling technique for speech preprocessing, designed to mitigate the impact of noise and channel distortions on acoustic features. This algorithm typically implements band-pass filtering in the logarithmic spectral domain to normalize spectral modulations. PLP features utilize Linear Predictive Coding (LPC)-based acoustic analysis, transforming speech signals through critical-band filtering, logarithmic compression, and cepstral transformation to generate more tractable feature representations. The implementation generally involves calculating LPC coefficients via autocorrelation or covariance methods, followed by cepstral analysis to derive perceptual linear predictive coefficients. By integrating RASTA-PLP and PLP features in speech processing and recognition systems, significant improvements in recognition accuracy and overall performance can be achieved through their complementary noise robustness and perceptual modeling capabilities.