EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation
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arXiv
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| Format: | Preprint |
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2024
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| author | Richter, Julius Wu, Yi-Chiao Krenn, Steven Welker, Simon Lay, Bunlong Watanabe, Shinji Richard, Alexander Gerkmann, Timo |
| author_facet | Richter, Julius Wu, Yi-Chiao Krenn, Steven Welker, Simon Lay, Bunlong Watanabe, Shinji Richard, Alexander Gerkmann, Timo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06185 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation Richter, Julius Wu, Yi-Chiao Krenn, Steven Welker, Simon Lay, Bunlong Watanabe, Shinji Richard, Alexander Gerkmann, Timo Audio and Speech Processing Machine Learning Sound 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. |
| title | EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation |
| topic | Audio and Speech Processing Machine Learning Sound |
| url | https://arxiv.org/abs/2406.06185 |