# Towards Improved Speech Recognition through Optimized Synthetic Data Generation

arXiv: https://arxiv.org/abs/2508.21631 · HTML: https://mlsanity.com/paper/2508.21631/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2025.

## Built on by (most cited first, 3 of 3)

- [How to Leverage Synthetic Speech for LLM-Based ASR Systems?](https://mlsanity.com/paper/2606.29031/) (2026)
- [When Synthetic Speech Is All You Have: Better Call GRPO](https://mlsanity.com/paper/2607.08409/) (2026)
- [Phoneme-guided TTS augmentation for ASR: A unified pipeline and multilingual evaluation](https://mlsanity.com/paper/2608.26697/) (2026)

## Abstract (arXiv)

Supervised training of speech recognition models requires access to transcribed audio data, which often is not possible due to confidentiality issues. Our approach to this problem is to generate synthetic audio from a text-only corpus using a state-of-the-art text-to-speech model with voice cloning capabilities. Our goal is to achieve automatic speech recognition (ASR) performance comparable to models trained on real data. We explore ways to optimize synthetic data generation through finetuning, filtering and evaluation, and its use for training an end-to-end encoder-decoder ASR model. Experiments were conducted using two datasets of spontaneous, conversational speech in Qu'ebec French. We show that improving data generation leads to large improvements in the final ASR system trained on synthetic data.