no code implementations • 17 Nov 2021 • Konstantinos Markopoulos, Nikolaos Ellinas, Alexandra Vioni, Myrsini Christidou, Panos Kakoulidis, Georgios Vamvoukakis, Georgia Maniati, June Sig Sung, Hyoungmin Park, Pirros Tsiakoulis, Aimilios Chalamandaris
In this paper, a text-to-rapping/singing system is introduced, which can be adapted to any speaker's voice.
no code implementations • 19 Nov 2021 • Alexandra Vioni, Myrsini Christidou, Nikolaos Ellinas, Georgios Vamvoukakis, Panos Kakoulidis, TaeHoon Kim, June Sig Sung, Hyoungmin Park, Aimilios Chalamandaris, Pirros Tsiakoulis
This paper presents a method for controlling the prosody at the phoneme level in an autoregressive attention-based text-to-speech system.
no code implementations • 19 Nov 2021 • Myrsini Christidou, Alexandra Vioni, Nikolaos Ellinas, Georgios Vamvoukakis, Konstantinos Markopoulos, Panos Kakoulidis, June Sig Sung, Hyoungmin Park, Aimilios Chalamandaris, Pirros Tsiakoulis
This paper presents a method for phoneme-level prosody control of F0 and duration on a multispeaker text-to-speech setup, which is based on prosodic clustering.
no code implementations • 29 Nov 2022 • Nikolaos Ellinas, Myrsini Christidou, Alexandra Vioni, June Sig Sung, Aimilios Chalamandaris, Pirros Tsiakoulis, Paris Mastorocostas
The final model enables fine-grained phoneme-level prosody control for all speakers contained in the training set, while maintaining the speaker identity.
no code implementations • 2 Feb 2024 • Panos Kakoulidis, Nikolaos Ellinas, Georgios Vamvoukakis, Myrsini Christidou, Alexandra Vioni, Georgia Maniati, Junkwang Oh, Gunu Jho, Inchul Hwang, Pirros Tsiakoulis, Aimilios Chalamandaris
In this paper, we propose a singing voice synthesis model, Karaoker-SSL, that is trained only on text and speech data as a typical multi-speaker acoustic model.
no code implementations • 2 Apr 2024 • Michael Mitsios, Georgios Vamvoukakis, Georgia Maniati, Nikolaos Ellinas, Georgios Dimitriou, Konstantinos Markopoulos, Panos Kakoulidis, Alexandra Vioni, Myrsini Christidou, Junkwang Oh, Gunu Jho, Inchul Hwang, Georgios Vardaxoglou, Aimilios Chalamandaris, Pirros Tsiakoulis, Spyros Raptis
We thus redefine the emotion labeling problem by shifting it from a traditional classification model to an ordinal classification one, where discrete emotions are arranged in a sequential order according to their valence levels.