Sixth International Conference on Spoken Language Processing
(ICSLP 2000)

Beijing, China
October 16-20, 2000

Large-Vocabulary Speech Recognition under Adverse Acoustic Environments

Li Deng, Alex Acero, Mike Plumpe, Xuedong Huang

Speech Technology Group, Microsoft Research, Redmond, WA, USA

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.


Full Paper

Bibliographic reference.  Deng, Li / Acero, Alex / Plumpe, Mike / Huang, Xuedong (2000): "Large-vocabulary speech recognition under adverse acoustic environments", In ICSLP-2000, vol.3, 806-809.