Iterative Self-Learning for Expressive Text-to-Speech Synthesis
Expressive text-to-speech (TTS) systems that use explicit conditioning labels provide direct and interpretable control over expressive attributes, in contrast to reference-based or prompting-based approaches, but require labeled data. Obtaining these labels at scale is costly and time-consuming, yet no prior semi-supervised framework addresses this specific bottleneck. Existing semi-supervised TTS methods instead target scarcity of paired speech-text data or transcriptions. To address the scarcity of expressive labels, we propose an Iterative Self-Learning (ISL) framework for expressive TTS, built on Invert-Classify, a classifier-free method that recovers discrete expressive labels by inv...
| Authors | Nicholas Sanders, Gustav Eje Henter, Simon King et al. |
|---|---|
| Primary category | eess.AS |
| Published | 16 August 2026 |
| Last updated at source | 16 August 2026 |
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