# High-Value Synthetic Supervision for Parameter-Efficient Adaptation of a Compact Japanese Speech Model

arXiv: https://arxiv.org/abs/2610.00026 · HTML: https://mlsanity.com/paper/2610.00026/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 2 Sep 2026.

## Builds on (most cited first, 5 of 5)

- [Distilling an End-to-End Voice Assistant Without Instruction Training Data](https://mlsanity.com/paper/2410.02678/) (2024)
- [LFM2 Technical Report](https://mlsanity.com/paper/2511.23404/) (2025)
- [Mind the Gap! Static and Interactive Evaluations of Large Audio Models](https://mlsanity.com/paper/2502.15919/) (2025)
- [Incubating Text Classifiers Following User Instruction with Nothing but LLM](https://mlsanity.com/paper/2404.10877/) (2024)
- [Open-world Multi-label Text Classification with Extremely Weak Supervision](https://mlsanity.com/paper/2407.05609/) (2024)

## Abstract (arXiv)

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 large paired gains over the same base; the full-versus-LoRA interval crosses zero, and differing optimization settings preclude an equivalence claim. This is a parameter-efficient capability-acquisition result, not a device-performance result: latency, memory, energy, and real-time factor were not measured. All evaluation speech and targets are synthetic, references are model-proposed, and the judge is uncalibrated. The evidence shows that a compact model can acquire a narrow audio-to-structure transformation from a few hundred provenance-linked synthetic examples; it does not establish clinical validity, real-speech transfer, or superiority to a clean cloud system.