Search Results for author: Karolos Nikitaras

Found 6 papers, 0 papers with code

Predicting phoneme-level prosody latents using AR and flow-based Prior Networks for expressive speech synthesis

no code implementations2 Nov 2022 Konstantinos Klapsas, Karolos Nikitaras, Nikolaos Ellinas, June Sig Sung, Inchul Hwang, Spyros Raptis, Aimilios Chalamandaris, Pirros Tsiakoulis

A large part of the expressive speech synthesis literature focuses on learning prosodic representations of the speech signal which are then modeled by a prior distribution during inference.

Expressive Speech Synthesis

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

Fine-grained Noise Control for Multispeaker Speech Synthesis

no code implementations11 Apr 2022 Karolos Nikitaras, Georgios Vamvoukakis, Nikolaos Ellinas, Konstantinos Klapsas, Konstantinos Markopoulos, Spyros Raptis, June Sig Sung, Gunu Jho, Aimilios Chalamandaris, Pirros Tsiakoulis

A text-to-speech (TTS) model typically factorizes speech attributes such as content, speaker and prosody into disentangled representations. Recent works aim to additionally model the acoustic conditions explicitly, in order to disentangle the primary speech factors, i. e. linguistic content, prosody and timbre from any residual factors, such as recording conditions and background noise. This paper proposes unsupervised, interpretable and fine-grained noise and prosody modeling.

Expressive Speech Synthesis

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