HebDB: a Weakly Supervised Dataset for Hebrew Speech Processing
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913424819093504 |
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| author | Turetzky, Arnon Tal, Or Segal-Feldman, Yael Dissen, Yehoshua Zeldes, Ella Roth, Amit Cohen, Eyal Shrem, Yosi Chernyak, Bronya R. Seleznova, Olga Keshet, Joseph Adi, Yossi |
| author_facet | Turetzky, Arnon Tal, Or Segal-Feldman, Yael Dissen, Yehoshua Zeldes, Ella Roth, Amit Cohen, Eyal Shrem, Yosi Chernyak, Bronya R. Seleznova, Olga Keshet, Joseph Adi, Yossi |
| contents | We present HebDB, a weakly supervised dataset for spoken language processing in the Hebrew language. HebDB offers roughly 2500 hours of natural and spontaneous speech recordings in the Hebrew language, consisting of a large variety of speakers and topics. We provide raw recordings together with a pre-processed, weakly supervised, and filtered version. The goal of HebDB is to further enhance research and development of spoken language processing tools for the Hebrew language. Hence, we additionally provide two baseline systems for Automatic Speech Recognition (ASR): (i) a self-supervised model; and (ii) a fully supervised model. We present the performance of these two methods optimized on HebDB and compare them to current multi-lingual ASR alternatives. Results suggest the proposed method reaches better results than the evaluated baselines considering similar model sizes. Dataset, code, and models are publicly available under https://pages.cs.huji.ac.il/adiyoss-lab/HebDB/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_07566 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HebDB: a Weakly Supervised Dataset for Hebrew Speech Processing Turetzky, Arnon Tal, Or Segal-Feldman, Yael Dissen, Yehoshua Zeldes, Ella Roth, Amit Cohen, Eyal Shrem, Yosi Chernyak, Bronya R. Seleznova, Olga Keshet, Joseph Adi, Yossi Computation and Language Sound Audio and Speech Processing We present HebDB, a weakly supervised dataset for spoken language processing in the Hebrew language. HebDB offers roughly 2500 hours of natural and spontaneous speech recordings in the Hebrew language, consisting of a large variety of speakers and topics. We provide raw recordings together with a pre-processed, weakly supervised, and filtered version. The goal of HebDB is to further enhance research and development of spoken language processing tools for the Hebrew language. Hence, we additionally provide two baseline systems for Automatic Speech Recognition (ASR): (i) a self-supervised model; and (ii) a fully supervised model. We present the performance of these two methods optimized on HebDB and compare them to current multi-lingual ASR alternatives. Results suggest the proposed method reaches better results than the evaluated baselines considering similar model sizes. Dataset, code, and models are publicly available under https://pages.cs.huji.ac.il/adiyoss-lab/HebDB/. |
| title | HebDB: a Weakly Supervised Dataset for Hebrew Speech Processing |
| topic | Computation and Language Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2407.07566 |