Reducing the Output-Mode Gap in Speech Language Models via Joint-Output On-Policy Distillation
Autoregressive generation of interleaved text and acoustic tokens is a common approach to spoken-response generation in speech large language models. Although this design enables streaming generation with explicit textual guidance, generated acoustic tokens become part of the context for subsequent text predictions. Given identical speech inputs, we observe markedly lower answer accuracy for the internal text generated in speech-to-text-and-speech (S2TS) mode than for speech-to-text (S2T) responses. We term this discrepancy the \emph{output-mode gap} (OMG). To reduce OMG, we propose \emph{Joint-Output On-Policy Distillation} (JO-OPD), which distills the model's stronger S2T policy into jo...
| Authors | Daxin Tan, Dehua Tao, Chengxi Deng et al. |
|---|---|
| Primary category | eess.AS |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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