How to Leverage Synthetic Speech for LLM-Based ASR Systems?
In regulated domains such as banking and healthcare, where privacy constraints make real speech costly to collect and retain, synthetic speech from modern text-to-speech (TTS) is an appealing alternative for training automatic speech recognition (ASR) without exposing sensitive customer recordings. Yet a persistent distributional gap between synthetic and real data limits how far it can replace genuine recordings. Prior work largely treats this gap as a black box to be engineered around, but in our work, we instead examine its origin directly by probing a SLAM-ASR architecture. Then, we localise where its LLM backbone separates real from synthetic speech and find the discriminative signal...
| Authors | Yanis Labrak, Dairazalia Sanchez-Cortes, Sergio Burdisso et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 June 2026 |
| Last updated at source | 9 July 2026 |
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