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ZeSTA framework enhances personalized speech synthesis with zero-shot TTS augmentation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ZeSTA, a new framework for improving personalized speech synthesis using zero-shot text-to-speech (ZS-TTS) as a data augmentation source. The method addresses the common issue of speaker similarity degradation when mixing synthetic and real speech data during fine-tuning. ZeSTA employs a domain-conditioned training approach that distinguishes between real and synthetic speech, coupled with oversampling of real data to stabilize adaptation, particularly in low-resource scenarios. AI

IMPACT This research could lead to more efficient and effective personalized voice generation, particularly in scenarios with limited training data.

RANK_REASON The cluster contains an academic paper detailing a new method for speech synthesis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ZeSTA framework enhances personalized speech synthesis with zero-shot TTS augmentation

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The cluster contains an academic paper detailing a new method for speech synthesis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Youngwon Choi, Jinwoo Oh, Hwayeon Kim, Hyeonyu Kim ·

    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 divers…

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