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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barker, J., Vincent, E., Ma, N., Christensen, C., Green, P.: The PASCAL CHiME speech separation and recognition challenge. Comput. Speech Lang. 27(3), 621–633 (2013)
Delcroix, M., Yoshioka, T., Ogawa, A., Kubo, Y., Fujimoto, M., Nobutaka, I., Kinoshita, K., Espi, M., Araki, S., Hori, T., Nakatani, T.: Strategies for distant speech recognition in reverberant environments. Comput. Speech Lang. (2015). doi:10.1186/s13634-015-0245-7
Giri, R., Seltzer, M., Droppo, J., Yu, D.: Improving speech recognition in reverberation using a room-aware deep neural network and multi-task learning. In: Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5014–5018 (2015)
Huang, X., Acero, A., Hong, H.W.: Spoken Language Processing: A Guide to Theory, Algorithm and System Development. Prentice Hall, Upper Suddle River, NJ (2001)
Kaldi-based baseline system for REVERB challenge. https://github.com/kaldi-asr/kaldi/tree/master/egs/reverb
Kinoshita, K., Delcroix, M., Yoshioka, T., Nakatani, T., Habets, E., Haeb-Umbach, R., Leutnant, V., Sehr, A., Kellermann, W., Maas, R., Gannot, S., Raj, B.: The REVERB challenge: a common evaluation framework for dereverberation and recognition of reverberant speech. In: Proceedings of Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (2013)
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.: A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research. EURASIP J. Adv. Signal Process. (2016). doi:10.1186/s13634-016-0306-6
LDC: Multi-channel WSJ audio. https://catalog.ldc.upenn.edu/LDC2014S03
LDC: WSJCAMO Cambridge read news. https://catalog.ldc.upenn.edu/LDC95S24
Lincoln, M., McCowan, I., Vepa, J., Maganti, H.K.: The multi-channel Wall Street Journal audio visual corpus (MC-WSJ-AV): specification and initial experiments. In: Proceedings of IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 357–362 (2005)
Naylor, P.A., Gaubitch, N.D.: Speech Dereverberation. Springer, Berlin (2010)
Pearce, D., Hirsch, H.G.: The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions. In: Proceedings of International Conference on Spoken Language Processing (ICSLP), pp. 29–32 (2000)
REVERB Challenge. http://reverb2014.dereverberation.com/
Robinson, T., Fransen, J., Pye, D., Foote, J., Renals, S.: WSJCAM0: a British English speech corpus for large vocabulary continuous speech recognition. In: Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 81–84 (1995)
Tachioka, Y., Narita, T., Weninger, F.J., Watanabe, S.: Dual system combination approach for various reverberant environments with dereverberation techniques. In: Proceedings of REVERB Challenge Workshop, p. 1.3 (2014)
Tashev, I.: Sound Capture and Processing. Wiley, Hoboken, NJ (2009)
Vincent, E., Araki, S., Theis, F.J., Nolte, G., Bofill, P., Sawada, H., Ozerov, A., Gowreesunker, B.V., Lutter, D.: The signal separation evaluation campaign (2007–2010): achievements and remaining challenges. Signal Process. 92, 1928–1936 (2012)
Wölfel, M., McDonough, J.: Distant Speech Recognition. Wiley, Hoboken, NJ (2009)
Yoshioka, T., Sehr, A., Delcroix, M., Kinoshita, K., Maas, R., Nakatani, T., Kellermann, W.: Making machines understand us in reverberant rooms: robustness against reverberation for automatic speech recognition. IEEE Signal Process. Mag. 29(6), 114–126 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Kinoshita, K. et al. (2017). The REVERB Challenge: A Benchmark Task for Reverberation-Robust ASR Techniques. In: Watanabe, S., Delcroix, M., Metze, F., Hershey, J. (eds) New Era for Robust Speech Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-64680-0_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-64680-0_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-64679-4
Online ISBN: 978-3-319-64680-0
eBook Packages: Computer ScienceComputer Science (R0)