Edinburgh Napier University

  Vector Taylor Series (VTS) is a powerful technique for robust ASR but, in its standard form, it can only be applied to log-filter bank and MFCC features. In earlier work, we presented a generalised VTS (gVTS) that extends the applicability of VTS to front-ends which employ a power transformation non-linearity. gVTS was shown to provide performance improvements in both clean and additive noise conditions. This paper makes two novel contributions. Firstly, while the previous gVTS formulation assumed that noise was purely additive, we now derive gVTS formulae for the case of speech in the presence of both additive noise and channel distortion. Second, we propose a novel iterative method for estimating the channel distortion which utilises gVTS itself and converges after a few iterations. Since the new gVTS blindly assumes the existence of both additive noise and channel effects, it is important not to introduce extra distortion when either are absent. Experimental results conducted on LVCSR Aurora-4 database show that the new formulation passes this test. In the presence of channel noise only, it provides relative WER reductions of up to 30% and 26%, compared with previous gVTS and multi-style training with cepstral mean normalisation, respectively.