arXiv:2606.29031v1 Announce Type: cross Abstract: 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 b
The increasing limitations and privacy concerns surrounding real speech data for ASR training are pushing researchers to find viable synthetic alternatives, making investigations into their effectiveness and limitations critical now.
This development can significantly reduce the cost and legal complexities of developing AI models in regulated industries, potentially democratizing advanced ASR capabilities.
The ability to more effectively leverage synthetic speech could reduce dependency on vast, privacy-sensitive real-world audio datasets for ASR, altering data collection paradigms in AI.
- · AI developers in regulated industries
- · Text-to-Speech (TTS) companies
- · Healthcare sector
- · Financial sector
- · Companies specializing solely in extensive real audio data collection
- · Traditional ASR data annotation services
Improved ASR systems become more accessible and deployable in privacy-sensitive environments.
Reduced barriers to entry for new AI-driven voice applications in sectors like banking and healthcare.
Enhanced AI voice assistants and interfaces become more prevalent, with lower development overhead, leading to broader adoption across various industries.
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Read at arXiv cs.AI