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
> Publications > Large-Vocabulary Speech Recognition under Adverse Acoustic Environments,