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

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]

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• codec-based SSL models
• Neural Audio Codecs
• self-supervised learning
• SSL pre-training

• paper
• other

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