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Communication Dans Un Congrès Année : 2023

BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models

Résumé

Self-supervised techniques for learning speech representations have been shown to develop linguistic competence from exposure to speech without the need for human labels. In order to fully realize the potential of these approaches and further our understanding of how infants learn language, simulations must closely emulate real-life situations by training on developmentally plausible corpora and benchmarking against appropriate test sets. To this end, we propose a language-acquisitionfriendly benchmark to probe spoken language models at the lexical and syntactic levels, both of which are compatible with the vocabulary typical of children's language experiences. This paper introduces the benchmark and summarizes a range of experiments showing its usefulness. In addition, we highlight two exciting challenges that need to be addressed for further progress: bridging the gap between text and speech and between clean speech and in-the-wild speech.
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Dates et versions

hal-04247612 , version 1 (18-10-2023)

Identifiants

Citer

Marvin Lavechin, Yaya Sy, Hadrien Titeux, María Andrea Cruz Blandón, Okko Räsänen, et al.. BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models. INTERSPEECH 2023, Aug 2023, Dublin, Ireland. pp.4588-4592, ⟨10.21437/Interspeech.2023-978⟩. ⟨hal-04247612⟩
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