HebDB: a Weakly Supervised Dataset for Hebrew Speech Processing

Arnon Turetzky, Or Tal, Yael Segal-Feldman, Yehoshua Dissen, Ella Zeldes +7 more
7/10/2024
cs.CLcs.SDeess.AS

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Abstract

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/.

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Cite this paper

@article{turetzky2024hebdb,
  title  = {HebDB: a Weakly Supervised Dataset for Hebrew Speech Processing},
  author = {Arnon Turetzky and Or Tal and Yael Segal-Feldman and Yehoshua Dissen and Ella Zeldes and Amit Roth and Eyal Cohen and Yosi Shrem and Bronya R. Chernyak and Olga Seleznova and Joseph Keshet and Yossi Adi},
  year   = {2024},
  eprint = {2407.07566},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.07566v1}
}

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