arXiv:2607.08409v1 Announce Type: new Abstract: LLM-based ASR adapted to regulated domains such as banking is bottlenecked by privacy: real speech is costly and legally constrained to collect, making synthetic text-to-speech (TTS) an attractive substitute. Yet synthetic speech stays acoustically mismatched with real recordings, and work on this gap has stayed within supervised fine-tuning (SFT). We instead turn to reinforcement learning, and show that Group Relative Policy Optimization (GRPO) extracts far more from the same synthetic speech than SFT. Synthetic-only adaptation of the model with
The increasing demand for LLM-based ASR in regulated domains and the inherent privacy challenges with real speech necessitate innovative solutions for synthetic data utilization. This research offers a timely advancement by applying reinforcement learning to overcome the limitations of supervised fine-tuning.
This development significantly enhances the utility of synthetic speech for training AI models in sensitive sectors, reducing compliance costs and accelerating AI adoption where real data is scarce or legally restricted. It directly addresses a critical bottleneck in the deployment of AI in privacy-conscious environments.
The ability to achieve better performance with synthetic speech through GRPO changes how ASR models can be adapted for regulated industries, shifting away from costly real speech collection towards more scalable and compliant synthetic data pipelines. It broadens the applicability of AI in sectors previously constrained by data access.
- · AI developers in regulated industries
- · Banking and finance sector
- · Healthcare sector
- · Data privacy solution providers
- · Companies reliant on expensive real speech data collection
- · Traditional supervised fine-tuning methodologies for ASR
Improved performance of ASR models in regulated domains using synthetic data, leading to wider AI adoption in these sectors.
Reduced operational costs and compliance burdens for institutions adopting AI in privacy-sensitive areas, fostering a more rapid digital transformation.
Potential for new business models specializing in high-quality, privacy-compliant synthetic data generation and AI model adaptation for specific industries.
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Read at arXiv cs.CL