High-Value Synthetic Supervision for Parameter-Efficient Adaptation of a Compact Japanese Speech Model
Private domain speech is difficult to collect and redistribute, while compact models need task-specific supervision. We study an auditable synthetic pipeline that maps Japanese care handoffs directly to six-field structured notes. Using 182 synthetic training and development clips, we adapt a 1.47B audio model by full fine-tuning and rank-16 LoRA. On a 39-clip scenario-seed-disjoint synthetic test, an unadapted model obtains a model-judged factuality-recall score of 0.0500, full tuning 0.8664, and LoRA 0.8461. LoRA reaches 97.7% of the full-tuning aggregate as a descriptive ratio while project telemetry reports 12.4M trainable parameters, about 0.85% of the backbone. Both adaptations show...
| Authors | Sidi Chang, Peiying Zhu |
|---|---|
| Primary category | cs.SD |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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