EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation
<|reference_start|>EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation: 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.<|reference_end|>
@article{richter2024ears:,
    title={EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech
  Enhancement and Dereverberation},
    author={Julius Richter, Yi-Chiao Wu, Steven Krenn, Simon Welker, Bunlong Lay,
  Shinji Watanabe, Alexander Richard, Timo Gerkmann},
    journal={arXiv preprint arXiv:2406.06185},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06185},
    primaryClass={eess.AS cs.LG cs.SD}
}