Distilling Text LMs into Speech-to-Speech LMs by Matching Internal Text Predictions
Text language models (LMs) acquire broad linguistic and factual capabilities through large-scale pretraining, while comparable capabilities remain difficult to learn from speech alone. Recent work has shown that knowledge distillation from pretrained text LMs offers a promising way to transfer their linguistic and factual capabilities to speech models. However, existing distillation approaches have largely focused on speech-input, text-output models. This idea has yet to be explored for {\it speech-to-speech} models, which operate on both speech input and speech output. In this work we introduce a speech-to-speech pretraining method that creates an internal text interface for knowledge tr...
| Authors | Ju-Chieh Chou, Jiawei Zhou, Karen Livescu |
|---|---|
| Primary category | cs.CL |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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