7 citations · 10 across the 10 of their papers we have counts for
10 papers
Pseudo-Cepstrum: Pitch Modification for Mel-Based Neural Vocoders
Nikolaos Ellinas, Alexandra Vioni, Panos Kakoulidis +8
This paper introduces a cepstrum-based pitch modification method that can be applied to any mel-spectrogram representation. As a result, this method is compatible with any mel-base…
MambaRate: Speech Quality Assessment Across Different Sampling Rates
Panos Kakoulidis, Iakovi Alexiou, Junkwang Oh +4
We propose MambaRate, which predicts Mean Opinion Scores (MOS) with limited bias regarding the sampling rate of the waveform under evaluation. It is designed for Track 3 of the Aud…
Investigating Disentanglement in a Phoneme-level Speech Codec for Prosody Modeling
Sotirios Karapiperis, Nikolaos Ellinas, Alexandra Vioni +4
Most of the prevalent approaches in speech prosody modeling rely on learning global style representations in a continuous latent space which encode and transfer the attributes of r…
Improved Text Emotion Prediction Using Combined Valence and Arousal Ordinal Classification
Michael Mitsios, Georgios Vamvoukakis, Georgia Maniati +13
Emotion detection in textual data has received growing interest in recent years, as it is pivotal for developing empathetic human-computer interaction systems. This paper introduce…
Low-Resource Cross-Domain Singing Voice Synthesis via Reduced Self-Supervised Speech Representations
Panos Kakoulidis, Nikolaos Ellinas, Georgios Vamvoukakis +8
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. It is a low-resourc…
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…
Generating Multilingual Gender-Ambiguous Text-to-Speech Voices
Konstantinos Markopoulos, Georgia Maniati, Georgios Vamvoukakis +9
The gender of any voice user interface is a key element of its perceived identity. Recently, there has been increasing interest in interfaces where the gender is ambiguous rather t…
Investigating Content-Aware Neural Text-To-Speech MOS Prediction Using Prosodic and Linguistic Features
Alexandra Vioni, Georgia Maniati, Nikolaos Ellinas +4
Current state-of-the-art methods for automatic synthetic speech evaluation are based on MOS prediction neural models. Such MOS prediction models include MOSNet and LDNet that use s…
Cross-lingual Text-To-Speech with Flow-based Voice Conversion for Improved Pronunciation
Nikolaos Ellinas, Georgios Vamvoukakis, Konstantinos Markopoulos +7
This paper presents a method for end-to-end cross-lingual text-to-speech (TTS) which aims to preserve the target language's pronunciation regardless of the original speaker's langu…