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A Noise-Robust ASR Front-End Using Wiener Filter Constructed from MMSE Estimation of Clean Speech and Noise

J. Wu, Jasha Droppo, Li Deng, and Alex Acero

Abstract

In this paper, we present a novel two-stage framework of designing a noise-robust front-end for automatic speech recognition. In the first stage, a parametric model of acoustic distortion is used to estimate the clean speech and noise spectra in a principled way so that no heuristic parameters need to set manually. To reduce possible flaws caused by the simplifying assumptions in the parametric model, a second-stage Wiener filtering is applied to further reduce the noise while preserving speech spectra unharmed. This front-end is evaluated on the Aurora2 task. For the multi-condition training scenario, a relative error reduction of 28.4% is achieved.

Details

Publication typeInproceedings
Published inProc. IEEE Workshop on Automatic Speech Recognition and Understanding
AddressU.S. Virgin Islands
PublisherInstitute of Electrical and Electronics Engineers, Inc.
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