Acoustic-to-Text KV Compression for Full-Duplex Speech Models
Full-duplex speech language models continuously accumulate acoustic key-value (KV) states, making long-running interactions memory-intensive. During listening, the model can finish processing an audio unit before the next arrives; we term the remaining interval listening-time slack. We propose acoustic-to-text KV compression, which introduces a transcription side channel to convert incoming speech into compact textual memory within this interval. When the cache exceeds a target budget during inference, older acoustic states are evicted while transcripts and recent acoustic context remain. We train the side channel with LoRA using cross-entropy on transcription segments. To preserve listen...
| Authors | Yejin Lee, Seungbeom Kim, Yongha Lee et al. |
|---|---|
| Primary category | cs.SD |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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