New framework tackles expressive label scarcity in text-to-speech synthesis

Researchers have developed an Iterative Self-Learning (ISL) framework to address the scarcity of labeled data for expressive text-to-speech (TTS) systems. This new method, built on Invert-Classify, iteratively pseudo-labels unlabeled speech using the current model and then retrains on the combined data. The framework progressively refines label quality and synthesis, showing improvements in pseudo-label accuracy and expressive adherence, particularly in low-resource scenarios. AI

IMPACT This research could lead to more efficient development of expressive TTS systems, reducing the need for extensive manual labeling.

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

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• Invert-Classify
• Iterative Self-Learning
• text-to-speech

• paper
• other

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