The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research
Open accessAbstract
In recent years, substantial progress has been made in the field of reverberant speech signal processing, including both single- and multichannel dereverberation techniques and automatic speech recognition (ASR) techniques that are robust to reverberation. In this paper, we describe the REVERB challenge, which is an evaluation campaign that was designed to evaluate such speech enhancement (SE) and ASR techniques to reveal the state-of-the-art techniques and obtain new insights regarding potential future research directions. Even though most existing benchmark tasks and challenges for distant speech processing focus on the noise robustness issue and sometimes only on a single-channel scenario, a particular novelty of the REVERB challenge is that it is carefully designed to test robustness against reverberation , based on both real, single-channel, and multichannel recordings . This challenge attracted 27 papers, which represent 25 systems specifically designed for SE purposes and 49 systems specifically designed for ASR purposes. This paper describes the problems dealt within the challenge, provides an overview of the submitted systems, and scrutinizes them to clarify what current processing strategies appear effective in reverberant speech processing.
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Cites
Evaluation of Objective Quality Measures for Speech Enhancement
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Huang, X., Acero, A., Hon, H.-W., & Reddy, R. (2001) · Cited by 1,593
An 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
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Kinoshita, K., Delcroix, M., Yoshioka, T., Nakatani, T., Habets, E., Haeb-Umbach, R., Leutnant, V., Sehr, A., Kellermann, W., Maas, R., et al. (2013) · 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics · doi:10.1109/waspaa.2013.6701894 · Cited by 376
Self-citationWSJCAMO: a British English speech corpus for large vocabulary continuous speech recognition
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Cited by
Attentive Statistics Pooling for Deep Speaker Embedding
Okabe, K., Koshinaka, T., & Shinoda, K. (2018) · Interspeech 2018 · doi:10.21437/interspeech.2018-993 · Cited by 514
“We also applied data augmentation to the training set in the following ways: (a) Additive noise: each segment was mixed with one of the noise samples in the PRISM corpus [20] (SNR: 8, 15, or 20dB), (b) Reverberation: each segment was convolved with one of the room impulse responses in the REVERB challenge data [21], (…”
MethodDetection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge
Mesaros, A., Heittola, T., Benetos, E., Foster, P., Lagrange, M., Virtanen, T., & Plumbley, M. D. (2018) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2017.2778423 · Cited by 298
Metadata
- Authors
- Kinoshita, K., Delcroix, M., Gannot, S., P. Habets, E. A., Haeb-Umbach, R., Kellermann, W., Leutnant, V., Maas, R., Nakatani, T., Raj, B., Sehr, A., & Yoshioka, T.
- Venue
- EURASIP Journal on Advances in Signal Processing
- Published
- 2016-01-18
- Volume
- 2016
- Issue
- 1
- ISSN
- 1110-8657 (ISSN-L)
- 1687-6180
- OpenAlex
- W2242685705
- Cited by
- 357
- References
- 17
- FWCI
- 46.94
- Type
- journal article
- Language
- en
- Open access
- gold
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- Kinoshita, K., Delcroix, M., Gannot, S., P. Habets, E. A., Haeb-Umbach, R., Kellermann, W., Leutnant, V., Maas, R., Nakatani, T., Raj, B., Sehr, A., & Yoshioka, T. (2016). A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research. EURASIP Journal on Advances in Signal Processing. https://doi.org/10.1186/s13634-016-0306-6
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