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An investigation of likelihood normalization for robust ASR
Year: | 2014 | ||||
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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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[Bibtex] |