@incollection{KinoshitaDelcroixGannotetal., author = {Kinoshita, Keisuke and Delcroix, Marc and Gannot, Sharon and Habets, Emanu{\"e}l A. P. and Haeb-Umbach, Reinhold and Kellermann, Walter and Leutnant, Volker and Maas, Roland and Nakatani, Tomohiro and Raj, Bhiksha and Sehr, Armin and Yoshioka, Takuya}, title = {The REVERB Challenge: A Benchmark Task for Reverberation-Robust ASR Techniques}, series = {New Era for Robust Speech Recognition}, volume = {27}, booktitle = {New Era for Robust Speech Recognition}, editor = {Watanabe, Shinji and Delcroix, Marc and Metze, Florian and Hershey, John R.}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-64679-4}, doi = {10.1007/978-3-319-64680-0_15}, pages = {345 -- 354}, 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.}, language = {en} } @article{KinoshitaDelcroixGannotetal., author = {Kinoshita, Keisuke and Delcroix, Marc and Gannot, Sharon and Habets, Emanuel A. P. and Haeb-Umbach, Reinhold and Kellermann, Walter and Leutnant, Volker and Maas, Roland and Nakatani, Tomohiro and Raj, Bhiksha and Sehr, Armin and Yoshioka, Takuya}, title = {A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research}, series = {Eurasip journal on advances in signal processing}, volume = {2016}, journal = {Eurasip journal on advances in signal processing}, publisher = {Springer Nature}, doi = {10.1186/s13634-016-0306-6}, abstract = {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.}, language = {en} }