Spatial diffuseness features for DNN-based speech recognition in noisy and reverberant environments

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Schwarz A, Hümmer C, Maas R, Kellermann W
Publisher: Institute of Electrical and Electronics Engineers Inc.
Publication year: 2015
Pages range: 4380-4384
ISBN: 978-1-4673-6997-8
ISSN: 2379-190X
Language: English


Abstract


We propose a spatial diffuseness feature for deep neural network (DNN)-based automatic speech recognition to improve recognition accuracy in reverberant and noisy environments. The feature is computed in real-time from multiple microphone signals without requiring knowledge or estimation of the direction of arrival, and represents the relative amount of diffuse noise in each time and frequency bin. It is shown that using the diffuseness feature as an additional input to a DNN-based acoustic model leads to a reduced word error rate for the REVERB challenge corpus, both compared to logmelspec features extracted from noisy signals, and features enhanced by spectral subtraction.


FAU Authors / FAU Editors

Hümmer, Christian
Kellermann, Walter Prof. Dr.-Ing.
Professur für Nachrichtentechnik
Professur für Nachrichtentechnik
Maas, Roland
Lehrstuhl für Multimediakommunikation und Signalverarbeitung
Schwarz, Andreas
Professur für Nachrichtentechnik


How to cite

APA:
Schwarz, A., Hümmer, C., Maas, R., & Kellermann, W. (2015). Spatial diffuseness features for DNN-based speech recognition in noisy and reverberant environments. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (pp. 4380-4384). Brisbane, AU: Institute of Electrical and Electronics Engineers Inc..

MLA:
Schwarz, Andreas, et al. "Spatial diffuseness features for DNN-based speech recognition in noisy and reverberant environments." Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brisbane Institute of Electrical and Electronics Engineers Inc., 2015. 4380-4384.

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Last updated on 2019-19-04 at 17:53