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Large-Vocabulary Speech Recognition under Adverse Acoustic Environments,

Li Deng, Alex Acero, M. Plumpe, and Xuedong Huang

Abstract

We report our recent work on noise-robust large-vocabulary speech recognition. Three key innovations are developed and evaluated in this work: 1) a new model learning paradigm that comprises a noise-insertion process followed by noise reduction; 2) a noise adaptive training algorithm that integrates noise reduction into probabilistic multi-style system training; and 3) a new algorithm (SPLICE) for noise reduction that makes no assumptions about noise stationarity. Evaluation on a large-vocabulary speech recognition task demonstrates significant and consistent error rate reduction using these techniques. The resulting error rate is shown to be lower than that achieved by the matched-noisy condition for both stationary and nonstationary natural, as well as simulated, noises.

Details

Publication typeInproceedings
Published inProc. Int. Conf. on Spoken Language Processing
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