# Synthetic Data Domain Adaptation for ASR via LLM-based Text and Phonetic Respelling Augmentation

arXiv: https://arxiv.org/abs/2603.16920 · HTML: https://mlsanity.com/paper/2603.16920/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2026.

## Built on by (most cited first, 1 of 1)

- [WhisperPipe: A Resource-Efficient Streaming Architecture for Real-Time Automatic Speech Recognition](https://mlsanity.com/paper/2604.25611/) (2026)

## Abstract (arXiv)

End-to-end automatic speech recognition often degrades on domain-specific data due to scarce in-domain resources. We propose a synthetic-data-based domain adaptation framework with two contributions: (1) a large language model (LLM)-based text augmentation pipeline with a filtering strategy that balances lexical diversity, perplexity, and domain-term coverage, and (2) phonetic respelling augmentation (PRA), a novel method that introduces pronunciation variability through LLM-generated orthographic pseudo-spellings. Unlike conventional acoustic-level methods such as SpecAugment, PRA provides phonetic diversity before speech synthesis, enabling synthetic speech to better approximate real-world variability. Experimental results across four domain-specific datasets demonstrate consistent reductions in word error rate, confirming that combining domain-specific lexical coverage with realistic pronunciation variation significantly improves ASR robustness.