Invariant-integration method for robust feature extraction in speaker-independent speech recognition

The vocal tract length (VTL) is one of the variabilities that speaker-independent automatic speech recognition (ASR) systems encounter. Standard methods to compensate for the effects of different VTLs within the processing stages of the ASR systems often have a high computational effort. By using an appropriate warping scheme for the frequency centers of the timefrequency analysis, a change in VTL can be approximately described by a translation in the subband-index space. We present a new type of features that is based on the principle of invariant integration, and an according feature selection method is described. ASR experiments show the increased robustness of the proposed features in comparison to standard MFCCs.