The REVERB challenge: A common evaluation framework for dereverberation and recognition of reverberant speech

K. Kinoshita, M. Delcroix, T. Yoshioka, T. Nakatani, E. Habets, R. Haeb-Umbach, V. Leutnant, A. Sehr, W. Kellermann, R. Maas, S. Gannot, B. Raj

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Recently, 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 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.
Original languageAmerican English
Title of host publicationIEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
StatePublished - 2013

Bibliographical note

Place of conference:New Paltz, USA

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