# A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data

arXiv: https://arxiv.org/abs/2501.12501 · HTML: https://mlsanity.com/paper/2501.12501/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2025.

## Built on by (most cited first, 2 of 2)

- [ContextASR-Bench: A Massive Contextual Speech Recognition Benchmark](https://mlsanity.com/paper/2507.05727/) (2025)
- [Enhancing ASR Performance in the Medical Domain for Dravidian Languages](https://mlsanity.com/paper/2604.19797/) (2026)

## Abstract (arXiv)

We introduce DAS (Domain Adaptation with Synthetic data), a novel domain adaptation framework for pre-trained ASR model, designed to efficiently adapt to various language-defined domains without requiring any real data. In particular, DAS first prompts large language models (LLMs) to generate domain-specific texts before converting these texts to speech via text-to-speech technology. The synthetic data is used to fine-tune Whisper with Low-Rank Adapters (LoRAs) for targeted domains such as music, weather, and sports. We introduce a novel one-pass decoding strategy that merges predictions from multiple LoRA adapters efficiently during the auto-regressive text generation process. Experimental results show significant improvements, reducing the Word Error Rate (WER) by 10% to 17% across all target domains compared to the original model, with minimal performance regression in out-of-domain settings (e.g., -1% on Librispeech test sets). We also demonstrate that DAS operates efficiently during inference, introducing an additional 9% increase in Real Time Factor (RTF) compared to the original model when inferring with three LoRA adapters.