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Speech enhancement with LSTM recurrent neural networks and its application to noise-robust ASR
Abstract : We evaluate some recent developments in recurrent neural network (RNN) based speech enhancement in the light of noise-robust automatic speech recognition (ASR). The proposed framework is based on Long Short-Term Memory (LSTM) RNNs which are discriminatively trained according to an optimal speech reconstruction objective. We demonstrate that LSTM speech enhancement, even when used ' na¨vely ' as front-end processing, delivers competitive results on the CHiME-2 speech recognition task. Furthermore, simple, feature-level fusion based extensions to the framework are proposed to improve the integration with the ASR back-end. These yield a best result of 13.76 % average word error rate, which is, to our knowledge, the best score to date.
Cited literature [17 references]
https://hal.inria.fr/hal-01163493
Contributor : Emmanuel Vincent <>
Submitted on : Saturday, June 13, 2015 - 10:46:45 AM
Last modification on : Monday, May 4, 2020 - 11:40:16 AM
Document(s) archivé(s) le : Monday, September 14, 2015 - 10:05:47 AM
Contributor : Emmanuel Vincent <>
Submitted on : Saturday, June 13, 2015 - 10:46:45 AM
Last modification on : Monday, May 4, 2020 - 11:40:16 AM
Document(s) archivé(s) le : Monday, September 14, 2015 - 10:05:47 AM
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weninger_LVA15.pdf
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