Article in Edited Volumes


The REVERB Challenge: A Benchmark Task for Reverberation-Robust ASR Techniques


Publication Details
Author(s): Kinoshita K, Delcroix M, Gannot S, Habets E, Haeb-Umbach R, Kellermann W, Leutnant V, Maas R, Nakatani T, Raj B, Sehr A, Yoshioka T
Editor(s): Shinji Watanabe, Marc Delcroix, Florian Metze, John R. Hershey
Title edited volumes: New Era for Robust Speech Recognition
Publisher: Springer International Publishing AG 2017
Publication year: 2017
Title of series: Exploiting Deep Learning
Pages range: 345 - 354
ISBN: 978-3-319-64680-0
Language: English

Abstract

The REVERB challenge is a benchmark task designed to evaluate reverberation-robust automatic speech recognition techniques under various conditions. A particular novelty of the REVERB challenge database is that it comprises both real reverberant speech recordings and simulated reverberant speech, both of which include tasks to evaluate techniques for 1-, 2-, and 8-microphone situations. In this chapter, we describe the problem of reverberation and characteristics of the REVERB challenge data, and finally briefly introduce some results and findings useful for reverberant speech processing in the current deep-neural-network era.



How to cite
APA: Kinoshita, K., Delcroix, M., Gannot, S., Habets, E., Haeb-Umbach, R., Kellermann, W.,... Yoshioka, T. (2017). The REVERB Challenge: A Benchmark Task for Reverberation-Robust ASR Techniques. In Shinji Watanabe, Marc Delcroix, Florian Metze, John R. Hershey (Eds.), New Era for Robust Speech Recognition (pp. 345 - 354). Springer International Publishing AG 2017.

MLA: Kinoshita, Keisuke, et al. "The REVERB Challenge: A Benchmark Task for Reverberation-Robust ASR Techniques." New Era for Robust Speech Recognition Ed. Shinji Watanabe, Marc Delcroix, Florian Metze, John R. Hershey, Springer International Publishing AG 2017, 2017. 345 - 354.

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