Search Results for author: Georgia Maniati

Found 8 papers, 0 papers with code

Generating Gender-Ambiguous Text-to-Speech Voices

no code implementations1 Nov 2022 Konstantinos Markopoulos, Georgia Maniati, Georgios Vamvoukakis, Nikolaos Ellinas, Karolos Nikitaras, Konstantinos Klapsas, Georgios Vardaxoglou, Panos Kakoulidis, June Sig Sung, Inchul Hwang, Aimilios Chalamandaris, Pirros Tsiakoulis, Spyros Raptis

While a female voice is a common choice, there is an increasing interest in alternative approaches where the gender is ambiguous rather than clearly identifying as female or male.

Learning utterance-level representations through token-level acoustic latents prediction for Expressive Speech Synthesis

no code implementations1 Nov 2022 Karolos Nikitaras, Konstantinos Klapsas, Nikolaos Ellinas, Georgia Maniati, June Sig Sung, Inchul Hwang, Spyros Raptis, Aimilios Chalamandaris, Pirros Tsiakoulis

We show that the fine-grained latent space also captures coarse-grained information, which is more evident as the dimension of latent space increases in order to capture diverse prosodic representations.

Disentanglement Expressive Speech Synthesis

Cross-lingual Text-To-Speech with Flow-based Voice Conversion for Improved Pronunciation

no code implementations31 Oct 2022 Nikolaos Ellinas, Georgios Vamvoukakis, Konstantinos Markopoulos, Georgia Maniati, Panos Kakoulidis, June Sig Sung, Inchul Hwang, Spyros Raptis, Aimilios Chalamandaris, Pirros Tsiakoulis

When used in a cross-lingual setting, acoustic features are initially produced with a native speaker of the target language and then voice conversion is applied by the same model in order to convert these features to the target speaker's voice.

Disentanglement Voice Conversion

Cross-lingual Low Resource Speaker Adaptation Using Phonological Features

no code implementations17 Nov 2021 Georgia Maniati, Nikolaos Ellinas, Konstantinos Markopoulos, Georgios Vamvoukakis, June Sig Sung, Hyoungmin Park, Aimilios Chalamandaris, Pirros Tsiakoulis

Subsequently, we fine-tune the model with very limited data of a new speaker's voice in either a seen or an unseen language, and achieve synthetic speech of equal quality, while preserving the target speaker's identity.

Speech Synthesis

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