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

• URL: http://arxiv.org/abs/2406.06185v2
• Date: Tue, 11 Jun 2024 21:18:14 GMT
• Title: EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation
• Authors: Julius Richter, Yi-Chiao Wu, Steven Krenn, Simon Welker, Bunlong Lay, Shinji Watanabe, Alexander Richard, Timo Gerkmann,
• Abstract summary: The EARS dataset comprises 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.
• Score: 83.29199726650899
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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