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

arxiv: 2406.06185 · v2 · pith:XJNYWA3K · submitted 2024-06-10 · eess.AS · cs.LG· cs.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

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classification eess.AS cs.LGcs.SD

keywords speechdatasetanechoicenhancementautomaticdatadereverberationdifferent

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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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