IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic speech recognition and speech variability: A review
Speech Communication
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The spectral effects of vocal tract length (VTL) differences are one reason for the lower recognition rate of today's speaker-independent automatic speech recognition (ASR) systems compared to speaker-dependent ones. By using certain types of filter banks the VTL-related effects can be described by a translation in subband-index space. In this paper, nonlinear translation-invariant transformations that originally have been proposed in the field of pattern recognition are investigated for their applicability in speaker-independent ASR tasks. It is shown that the combination of different types of such transformations leads to features that are more robust against VTL changes than the standard mel-frequency cepstral coefficients and that they almost yield the performance of vocal tract length normalization without any adaption to individual speakers.