EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation

Alexander Richard; Bunlong Lay; Julius Richter; Shinji Watanabe; Simon Welker; Steven Krenn; Timo Gerkmann; Yi-Chiao Wu

EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation

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arxiv 2406.06185 v2 pith:XJNYWA3K submitted 2024-06-10 eess.AS cs.LGcs.SD

EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation

Julius Richter , Yi-Chiao Wu , Steven Krenn , Simon Welker , Bunlong Lay , Shinji Watanabe , Alexander Richard , Timo Gerkmann This is my paper

classification eess.AS cs.LGcs.SD

keywords speechdatasetanechoicenhancementautomaticdatadereverberationdifferent

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

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We release the EARS (Expressive Anechoic Recordings of Speech) dataset, a high-quality speech dataset comprising 107 speakers from diverse backgrounds, totaling in 100 hours of clean, anechoic speech data. The dataset covers a large range of different speaking styles, including emotional speech, different reading styles, non-verbal sounds, and conversational freeform speech. We benchmark various methods for speech enhancement and dereverberation on the dataset and evaluate their performance through a set of instrumental metrics. In addition, we conduct a listening test with 20 participants for the speech enhancement task, where a generative method is preferred. We introduce a blind test set that allows for automatic online evaluation of uploaded data. Dataset download links and automatic evaluation server can be found online.

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eess.AS 2026-05 unverdicted novelty 6.0

JASTIN is an instruction-driven audio evaluation system that achieves state-of-the-art correlation with human ratings on speech, sound, music, and out-of-domain tasks without task-specific retraining.