@article {2025,
	title = {PRESENT: Zero-Shot Text-to-Prosody Control},
	journal = {IEEE Signal Processing Letters},
	year = {2025},
	abstract = {Current strategies for achieving fine-grained prosody control in speech synthesis entail extracting additional style embeddings or adopting more complex architectures. To enable zero-shot application of pretrained text-to-speech (TTS) models, we present PRESENT (PRosody Editing without Style Embeddings or New Training), which exploits explicit prosody prediction in FastSpeech2-based models by modifying the inference process directly. We apply our text-to-prosody framework to zero-shot language transfer using a JETS model exclusively trained on English LJSpeech data. We obtain character error rates (CER) of 12.8\%, 18.7\% and 5.9\% for German, Hungarian and Spanish respectively, beating the previous state-of-the-art CER by over 2x for all three languages. Furthermore, we allow subphoneme-level control, a first in this field. To evaluate its effectiveness, we show that PRESENT can improve the prosody of questions, and use it to generate Mandarin, a tonal language where vowel pitch varies at subphoneme level. We attain 25.3\% hanzi CER and 13.0\% pinyin CER with the JETS model. All our code and audio samples are available online.},
	doi = {10.1109/LSP.2025.3528359},
	url = {https://ieeexplore.ieee.org/document/10838710},
	author = {P. Lam and H. Zhang and N. F. Chen and B. Sisman and D. Herremans}
}
