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

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 coefficients. This approach can be extended to several levels of different resolutions. Multi-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 a connected digit recognition task