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1 PAROLE - Analysis, perception and recognition of speech Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery 2 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery

Abstract : We consider the problem of uncertainty estimation for noise-robust ASR. Existing uncertainty estimation techniques improve ASR accuracy but they still exhibit a gap compared to the use of oracle uncertainty. This comes partly from the highly non-linear feature transformation and from ad-ditional assumptions such as Gaussian distribution and independence between frequency bins in the spectral domain. In this paper, we propose a method to rescale the estimated feature-domain full uncertainty covariance matrix in a state-dependent fashion according to a discriminative criterion. The state-dependent and feature index-dependent scaling factors are learned from development data. Experimental evaluation on Track 1 of the 2nd CHiME challenge data shows that discriminative rescaling leads to better results than generative rescaling. Moreover, discriminative rescaling of the Wiener uncertainty estimator leads to 12% relative word error rate reduction compared to discriminative rescaling of the alternative estimator in 1

Keywords : discriminativeadaptation IndexTerms—Automaticspeechrecognition noisero-bustness uncertaintyhandling





Author: Dung Tien Tran - Emmanuel Vincent - Denis Jouvet -

Source: https://hal.archives-ouvertes.fr/



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