TY - CHAP A1 - Kinoshita, Keisuke A1 - Delcroix, Marc A1 - Gannot, Sharon A1 - Habets, Emanuël A. P. A1 - Haeb-Umbach, Reinhold A1 - Kellermann, Walter A1 - Leutnant, Volker A1 - Maas, Roland A1 - Nakatani, Tomohiro A1 - Raj, Bhiksha A1 - Sehr, Armin A1 - Yoshioka, Takuya ED - Watanabe, Shinji ED - Delcroix, Marc ED - Metze, Florian ED - Hershey, John R. T1 - The REVERB Challenge: A Benchmark Task for Reverberation-Robust ASR Techniques T2 - New Era for Robust Speech Recognition N2 - 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. Y1 - 2017 SN - 978-3-319-64679-4 U6 - https://doi.org/10.1007/978-3-319-64680-0_15 VL - 27 SP - 345 EP - 354 PB - Springer CY - Cham ER - TY - JOUR A1 - Kinoshita, Keisuke A1 - Delcroix, Marc A1 - Gannot, Sharon A1 - Habets, Emanuel A. P. A1 - Haeb-Umbach, Reinhold A1 - Kellermann, Walter A1 - Leutnant, Volker A1 - Maas, Roland A1 - Nakatani, Tomohiro A1 - Raj, Bhiksha A1 - Sehr, Armin A1 - Yoshioka, Takuya T1 - A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research JF - Eurasip journal on advances in signal processing N2 - 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. KW - Automatic speech recognition KW - Dereverberation KW - Evaluation campaign KW - QUALITY KW - REVERB challenge KW - Reverberation Y1 - 2016 U6 - https://doi.org/10.1186/s13634-016-0306-6 VL - 2016 PB - Springer Nature ER - TY - CHAP A1 - Yoshioka, Takuya A1 - Sehr, Armin A1 - Delcroix, Marc A1 - Kinoshita, Keisuke A1 - Maas, Roland A1 - Nakatani, Tomohiro A1 - Kellermann, Walter T1 - Survey on approaches to speech recognition in reverberant environments T2 - Asia-Pacific Signal & Information Processing Association annual summit and conference (APSIPA ASC), 2012 : Hollywood, California, USA, 3 - 6 Dec. 2012 N2 - This paper overviews the state of the art in reverberant speech processing from the speech recognition viewpoint. First, it points out that the key to successful reverberant speech recognition is to account for long-term dependencies between reverberant observations obtained from consecutive time frames. Then, a diversity of approaches that exploit the long-term dependencies in various ways is described, ranging from signal and feature dereverberation to acoustic model compensation tailored to reverberation. A framework for classifying those approaches is presented to highlight similarities and differences between them. KW - Speech recognition KW - Hidden Markov models KW - Reverberation KW - Speech KW - Vectors KW - Speech processing Y1 - 2012 SN - 978-0-6157-0050-2 PB - IEEE ER - TY - CHAP A1 - Sehr, Armin A1 - Yoshioka, Takuya A1 - Delcroix, Marc A1 - Kinoshita, Keisuke A1 - Nakatani, Tomohiro A1 - Maas, Roland A1 - Kellermann, Walter T1 - Conditional emission densities for combining speech enhancement and recognition systems T2 - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 25 - 29 August 2013, Lyon, France N2 - A novel framework based on conditional emission densities for hidden Markov models (HMMs) is proposed in this contribution to integrate speech enhancement systems with automatic speech recognition systems. In the training phase, the observed feature vectors, corrupted by background noise and reverberation, together with estimates for the interference as provided by the speech enhancement system are used for training joint densities of the observations and the interference estimates. In the decoding phase, the joint densities are transformed to conditional densities of the observed features given the interference estimates. Thus, front end processing can be exploited for obtaining interference estimates, and the estimation errors can be modeled very effectively in a data-driven way. Connected digit recognition experiments in a simulated reverberant environment show the potential of the proposed approach: HMMs with the proposed conditional densities outperform various configurations of conventional HMMs in the logarithmic melspectral domain. This is a first step towards using conditional densities for creating synergies between front end and back end. Index Terms: speech enhancement, robust speech recognition, dereverberation, conditional HMM emission densities, frame- by-frame model adaptation. Y1 - 2013 U6 - https://doi.org/10.21437/Interspeech.2013-265 SP - 3502 EP - 3506 PB - ISCA ER -