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An investigation of likelihood normalization for robust ASR

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In Proceedings

 

Year: 2014
Authors: Vincent E.; Aggelos Gkiokas; D. Schnitzer; Flexer A.
Book title: Interspeech 2014
Date: 1-18 September
Abstract:
Noise-robust automatic speech recognition (ASR) systems rely on feature and/or model compensation. Existing compensation techniques typically operate on the features or on the parameters of the acoustic models themselves. By contrast, a number of normalization techniques have been defined in the field of speaker verification that operate on the resulting log-likelihood scores. In this paper, we provide a theoretical motivation for likelihood normalization due to the so-called "hubness" phenomenon and we evaluate the benefit of several normalization techniques on ASR accuracy for the 2nd CHiME Challenge task. We show that symmetric normalization (S-norm) reduces the relative error rate by 43% alone and by 10% after feature and model compensation
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