@inproceedings{11841,
  abstract     = {{Recently, substantial progress has been made in the field of reverberant speech signal processing, including both single- and multichannel de-reverberation techniques, and automatic speech recognition (ASR) techniques robust to reverberation. To evaluate state-of-the-art algorithms and obtain new insights regarding potential future research directions, we propose a common evaluation framework including datasets, tasks, and evaluation metrics for both speech enhancement and ASR techniques. The proposed framework will be used as a common basis for the REVERB (REverberant Voice Enhancement and Recognition Benchmark) challenge. This paper describes the rationale behind the challenge, and provides a detailed description of the evaluation framework and benchmark results.}},
  author       = {{Kinoshita, Keisuke and Delcroix, Marc and Yoshioka, Takuya and Nakatani, Tomohiro and Habets, Emanuel and Haeb-Umbach, Reinhold and Leutnant, Volker and Sehr, Armin and Kellermann, Walter and Maas, Roland and Gannot, Sharon and Raj, Bhiksha}},
  booktitle    = {{ IEEE Workshop on Applications of Signal Processing to Audio and Acoustics }},
  keywords     = {{Reverberant speech, dereverberation, ASR, evaluation, challenge}},
  pages        = {{ 22--23 }},
  title        = {{{The reverb challenge: a common evaluation framework for dereverberation and recognition of reverberant speech}}},
  year         = {{2013}},
}