Datasets > Modality > Audio > ReVerb Challenge (REverberant Voice Enhancement and Recognition Benchmark)

ReVerb Challenge (REverberant Voice Enhancement and Recognition Benchmark)

Introduced by Keisuke Kinoshita et al. in The REVERB challenge: A common evaluation framework for dereverberation and recognition of reverberant speech

The REVERB (REverberant Voice Enhancement and Recognition Benchmark) challenge is a benchmark for evaluation of automatic speech recognition techniques. The challenge assumes the scenario of capturing utterances spoken by a single stationary distant-talking speaker with 1-channe, 2-channel or 8-channel microphone-arrays in reverberant meeting rooms. It features both real recordings and simulated data.

The challenge constis of speech enhancement and automatic speech recognition tasks in reverberant environments. The speech enhancement challenge task consists of enhancing noisy reverberant speech with single-/multi-channel speech enhancement techniques, and evaluating the enhanced data in terms of objective and subjective evaluation metrics. The automatic speech recognition challenge task consists of improving the recognition accuracy of the same reverberant speech. The background noise is mostly stationary and the signal-to-noise ratio is modest.