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Large-vocabulary speech recognition under adverse acoustic environments
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. 1.
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Recent advances in deep learning for speech research at Microsoft
Deng, L., Li, J., Huang, J.-T., Yao, K., Yu, D., Seide, F., Seltzer, M., Zweig, G., He, X., Williams, J., et al. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639345 · Cited by 801
Self-citationAn investigation of deep neural networks for noise robust speech recognition
Seltzer, M. L., Yu, D., & Wang, Y. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639100 · Cited by 640
Data Augmentation for Deep Neural Network Acoustic Modeling
Cui, X., Goel, V., & Kingsbury, B. (2015) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2015.2438544 · Cited by 376
HMM adaptation using vector taylor series for noisy speech recognition
Acero, A., Deng, L., Kristjansson, T., & Zhang, J. (2000) · 6th International Conference on Spoken Language Processing (ICSLP 2000) · doi:10.21437/icslp.2000-672 · Cited by 278
Self-citationFundamentals of speech recognition
Li, J., Deng, L., Haeb-Umbach, R., & Gong, Y. (2016) · Robust Automatic Speech Recognition · doi:10.1016/b978-0-12-802398-3.00002-7 · Cited by 266
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