NDLI: Feature enhancement based on generative-discriminative hybrid approach with gmms and DNNS for noise robust speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Fujimoto, M. Nakatani, T.
Copyright Year	2015
Description	Author affiliation: NTT Commun. Sci. Labs., NTT Corp., Kyoto, Japan (Fujimoto, M.; Nakatani, T.)
Abstract	This paper presents a technique that combines generative and discriminative approaches with Gaussian mixture models (GMMs) and deep neural networks (DNNs) for model-based feature enhancement. Typical model-based feature enhancement employs a generative model approach. The enhanced features are obtained by using the weighted sum of linear transformations given by each Gaussian component contained in GMMs and corresponding posterior probabilities. The computation of posterior probabilities is a crucial factor for this kind of feature enhancement, and can also be formulated as the class discrimination problem of observed noisy features. The prominent discriminability of DNNs is a well-known solution to this discrimination problem. Therefore, we propose the use of DNNs for computing posterior probabilities. The proposed method incorporates the benefit of the discriminative approach into the generative approach. For AURORA2 task evaluations, the proposed method provided noticeable improvements compared with results obtained using the conventional generative model approach.
Starting Page	5019
Ending Page	5023
File Size	592787
Page Count	5
File Format	PDF
ISBN	9781467369978
DOI	10.1109/ICASSP.2015.7178926
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-19
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech Speech recognition Estimation Computational modeling Noise reduction unsupervised modeling feature enhancement generative-discriminative hybrid approach deep neural networks
Content Type	Text
Resource Type	Article

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