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Speech learning models show language dependence on pre-training, not codec

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the language sensitivity of self-supervised learning (SSL) models that utilize neural audio codec tokens. The study found that while the performance of these codec-based SSL models is not significantly affected by the language used to train the neural audio codec itself, it is highly dependent on the language used for SSL pre-training. This suggests that a single neural audio codec can be effectively reused across different languages, but aligning the SSL pre-training language with the target language is critical for optimal results. AI

RANK_REASON Research paper analyzing a specific aspect of self-supervised speech learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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Speech learning models show language dependence on pre-training, not codec

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Research paper analyzing a specific aspect of self-supervised speech learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Daigo Takizawa, Tomohiko Nakamura, Samuele Cornell, William Chen, Satoru Fukayama, Shinji Watanabe ·

    Dissecting Sensitivity to Training Language in Self-Supervised Speech Learning Using Neural Audio Codec Tokens

    arXiv:2607.26350v1 Announce Type: cross Abstract: Neural audio codecs (NACs) have become popular for obtaining speech representations as discrete tokens. Beyond compression, discrete tokens can be used to train self-supervised learning (SSL) models. Such models, referred to as co…

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