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In this paper, we develop di€erent mathematical models in the framework of the multi-stream paradigm for noise robust automatic speech recognition (ASR), and discuss their close relationship with human speech perception. Largely inspired by Fletcher's ``product-of-errors'' rule (PoE rule) in psychoacoustics, multi-band ASR aims for robustness to data(More)
  • Astrid Hagen, Andrew Morris, Herv E Bourlard, Andrew Morris Herv, Bourlard, Mons +3 others
  • 1998
In this report, we investigate and compare diierent subband-based Automatic Speech Recognition (ASR) approaches, including an original approach, referred to as the \full combination approach", based on an estimate of the (noise-) weighted sum of posterior probabilities for all possible subband combinations. We show that the proposed estimate is a good(More)
Feature projection by non-linear discriminant analysis (NLDA) can substantially increase classification performance. In automatic speech recognition (ASR) the projection provided by the pre-squashed outputs from a one hidden layer multi-layer perceptron (MLP) trained to recognise speech sub-units (phonemes) has previously been shown to significantly(More)
Traditional microphone array speech recognition systems simply recognise the enhanced output of the array. As the level of signal enhancement depends on the number of microphones, such systems do not achieve acceptable speech recognition performance for arrays having only a few microphones. For small microphone arrays, we instead propose using the enhanced(More)
The performance of most ASR systems degrades rapidly with data mismatch relative to the data used in training. Under many realistic noise conditions a significant proportion of the spectral representation of a speech signal, which is highly redundant, remains uncorrupted. In the " missing feature " approach to this problem mismatching data is simply(More)