PRESENT: Zero-Shot Text-to-Prosody Control
<|reference_start|>PRESENT: Zero-Shot Text-to-Prosody Control: 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.<|reference_end|>
@article{lam2024present:,
    title={PRESENT: Zero-Shot Text-to-Prosody Control},
    author={Perry Lam, Huayun Zhang, Nancy F. Chen, Berrak Sisman, Dorien
  Herremans},
    journal={arXiv preprint arXiv:2408.06827},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06827},
    primaryClass={eess.AS cs.LG}
}