Prosodic Feature
Prosodic features, encompassing aspects of speech like pitch, intensity, and rhythm, are crucial for conveying meaning and emotion beyond the literal words spoken. Current research focuses on accurately modeling and manipulating these features in applications such as speech synthesis, editing, and voice conversion, often employing deep learning models like diffusion models, variational autoencoders, and actor-critic reinforcement learning. This work is significant for improving the naturalness and expressiveness of synthetic speech, enhancing accessibility for individuals with communication disorders, and advancing our understanding of human communication itself.
Papers
Learning utterance-level representations through token-level acoustic latents prediction for Expressive Speech Synthesis
Karolos Nikitaras, Konstantinos Klapsas, Nikolaos Ellinas, Georgia Maniati, June Sig Sung, Inchul Hwang, Spyros Raptis, Aimilios Chalamandaris, Pirros Tsiakoulis
Investigating Content-Aware Neural Text-To-Speech MOS Prediction Using Prosodic and Linguistic Features
Alexandra Vioni, Georgia Maniati, Nikolaos Ellinas, June Sig Sung, Inchul Hwang, Aimilios Chalamandaris, Pirros Tsiakoulis
Acoustic Modeling for End-to-End Empathetic Dialogue Speech Synthesis Using Linguistic and Prosodic Contexts of Dialogue History
Yuto Nishimura, Yuki Saito, Shinnosuke Takamichi, Kentaro Tachibana, Hiroshi Saruwatari
Automatic Prosody Annotation with Pre-Trained Text-Speech Model
Ziqian Dai, Jianwei Yu, Yan Wang, Nuo Chen, Yanyao Bian, Guangzhi Li, Deng Cai, Dong Yu