ZeSTA: Zero-Shot TTS Augmentation with Domain-Conditioned Training for Data-Efficient Personalized Speech Synthesis
arXiv:2603.04219v2 Announce Type: replace-cross Abstract: We investigate the use of zero-shot text-to-speech (ZS-TTS) as a data augmentation source for low-resource personalized speech synthesis. While synthetic augmentation can provide linguistically rich and phonetically diverse speech, naively mixing large amounts of synthetic speech with limited real recordings often leads to speaker similarity degradation during fine-tuning. To address this issue, we propose ZeSTA, a simple domain-conditioned training framework that distinguishes real and synthetic speech via a lightweight domain embeddin
Advances in zero-shot text-to-speech models are maturing, creating new opportunities for data augmentation in personalized speech synthesis.
This development significantly lowers the data barrier for creating high-quality personalized AI voices, accelerating the broader deployment of advanced conversational AI.
It becomes much easier and faster to create custom AI voices with limited real-world audio data, reducing costs and increasing accessibility for diverse applications.
- · AI voice synthesis platforms
- · Content creators (podcasts, audiobooks)
- · Customer service & virtual assistants
- · Accessibility technology developers
- · Voice actors (for certain short form applications)
- · Companies reliant on large human voice datasets
The cost and time required for creating custom AI voices are substantially reduced, making personalized speech synthesis more ubiquitous.
This democratizes access to advanced voice AI, enabling smaller entities and individual creators to deploy sophisticated voice applications previously limited to large enterprises.
The proliferation of indistinguishable synthetic voices could intensify concerns over deepfakes and the authenticity of digital audio, requiring new verification technologies.
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Read at arXiv cs.AI