Hierarchical subband linear predictive cepstral (HSLPC) features for HMM-based speech recognition

In this paper, a new approach for linear prediction (LP) analysis is explored, where predictor can be computed from a mel-warped subband-based autocorrelation functions obtained from the power spectrum. For spectral representation a set of multi-resolution cepstral features are proposed. The general idea is to divide up the full frequency-band into several subbands, perform the IDFT on the mel power spectrum for each subband, followed by Durbin's algorithm and the standard conversion from LP to cepstral coe cients. This approach can be extended to several levels of di erent resolutions. Muti-resolution feature vectors, formed by concatenation of the subband cepstral features into an extended feature vector, are shown to yield better performance than the conventional mel-warped LPCCs over the full voice-bandwidth for connected digit recognition task.