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ASR-Generated Text for Language Model Pre-training Applied to Speech Tasks

Abstract : We aim at improving spoken language modeling (LM) using very large amount of automatically transcribed speech. We leverage the INA (French National Audiovisual Institute 1) collection and obtain 19GB of text after applying ASR on 350,000 hours of diverse TV shows. From this, spoken language models are trained either by fine-tuning an existing LM (FlauBERT 2) or through training a LM from scratch. New models (FlauBERT-Oral) are shared with the community and evaluated for 3 downstream tasks: spoken language understanding, classification of TV shows and speech syntactic parsing. Results show that FlauBERT-Oral can be beneficial compared to its initial FlauBERT version demonstrating that, despite its inherent noisy nature, ASR-generated text can be used to build spoken language models.
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Contributor : Valentin Pelloin Connect in order to contact the contributor
Submitted on : Tuesday, September 6, 2022 - 3:07:15 PM
Last modification on : Friday, October 7, 2022 - 4:37:07 AM


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  • HAL Id : hal-03770506, version 1
  • ARXIV : 2207.01893


Valentin Pelloin, Franck Dary, Nicolas Hervé, Benoît Favre, Nathalie Camelin, et al.. ASR-Generated Text for Language Model Pre-training Applied to Speech Tasks. Interspeech 2022, Sep 2022, Incheon, South Korea. ⟨hal-03770506⟩



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