27 citations · 37 across the 6 of their papers we have counts for
6 papers
Predicting phoneme-level prosody latents using AR and flow-based Prior Networks for expressive speech synthesis
Konstantinos Klapsas, Karolos Nikitaras, Nikolaos Ellinas +5
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 i…
Learning utterance-level representations through token-level acoustic latents prediction for Expressive Speech Synthesis
Karolos Nikitaras, Konstantinos Klapsas, Nikolaos Ellinas +6
This paper proposes an Expressive Speech Synthesis model that utilizes token-level latent prosodic variables in order to capture and control utterance-level attributes, such as cha…
Fine-grained Noise Control for Multispeaker Speech Synthesis
Karolos Nikitaras, Georgios Vamvoukakis, Nikolaos Ellinas +7
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 th…
Self-supervised learning for robust voice cloning
Konstantinos Klapsas, Nikolaos Ellinas, Karolos Nikitaras +8
Voice cloning is a difficult task which requires robust and informative features incorporated in a high quality TTS system in order to effectively copy an unseen speaker's voice. I…
SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis
Georgia Maniati, Alexandra Vioni, Nikolaos Ellinas +6
In this work, we present the SOMOS dataset, the first large-scale mean opinion scores (MOS) dataset consisting of solely neural text-to-speech (TTS) samples. It can be employed to…
Word-Level Style Control for Expressive, Non-attentive Speech Synthesis
Konstantinos Klapsas, Nikolaos Ellinas, June Sig Sung +2
This paper presents an expressive speech synthesis architecture for modeling and controlling the speaking style at a word level. It attempts to learn word-level stylistic and proso…