66 citations · 217 across the 27 of their papers we have counts for
33 papers
Investigating self-supervised features for expressive, multilingual voice conversion
Álvaro Martín-Cortinas, Daniel Sáez-Trigueros, Grzegorz Beringer +7
Voice conversion (VC) systems are widely used for several applications, from speaker anonymisation to personalised speech synthesis. Supervised approaches learn a mapping between d…
Lightweight End-to-end Text-to-speech Synthesis for low resource on-device applications
Biel Tura Vecino, Adam Gabryś, Daniel Mątwicki +4
Recent works have shown that modelling raw waveform directly from text in an end-to-end (E2E) fashion produces more natural-sounding speech than traditional neural text-to-speech (…
Enhancing the Stability of LLM-based Speech Generation Systems through Self-Supervised Representations
Álvaro Martín-Cortinas, Daniel Sáez-Trigueros, Iván Vallés-Pérez +6
Large Language Models (LLMs) are one of the most promising technologies for the next era of speech generation systems, due to their scalability and in-context learning capabilities…
Multilingual context-based pronunciation learning for Text-to-Speech
Giulia Comini, Manuel Sam Ribeiro, Fan Yang +2
Phonetic information and linguistic knowledge are an essential component of a Text-to-speech (TTS) front-end. Given a language, a lexicon can be collected offline and Grapheme-to-P…
Comparing normalizing flows and diffusion models for prosody and acoustic modelling in text-to-speech
Guangyan Zhang, Thomas Merritt, Manuel Sam Ribeiro +10
Neural text-to-speech systems are often optimized on L1/L2 losses, which make strong assumptions about the distributions of the target data space. Aiming to improve those assumptio…
Improving grapheme-to-phoneme conversion by learning pronunciations from speech recordings
Manuel Sam Ribeiro, Giulia Comini, Jaime Lorenzo-Trueba
The Grapheme-to-Phoneme (G2P) task aims to convert orthographic input into a discrete phonetic representation. G2P conversion is beneficial to various speech processing application…
Low-data? No problem: low-resource, language-agnostic conversational text-to-speech via F0-conditioned data augmentation
Giulia Comini, Goeric Huybrechts, Manuel Sam Ribeiro +2
The availability of data in expressive styles across languages is limited, and recording sessions are costly and time consuming. To overcome these issues, we demonstrate how to bui…
Computer-assisted Pronunciation Training -- Speech synthesis is almost all you need
Daniel Korzekwa, Jaime Lorenzo-Trueba, Thomas Drugman +1
The research community has long studied computer-assisted pronunciation training (CAPT) methods in non-native speech. Researchers focused on studying various model architectures, s…
Voice Filter: Few-shot text-to-speech speaker adaptation using voice conversion as a post-processing module
Adam Gabryś, Goeric Huybrechts, Manuel Sam Ribeiro +6
State-of-the-art text-to-speech (TTS) systems require several hours of recorded speech data to generate high-quality synthetic speech. When using reduced amounts of training data,…
Cross-speaker style transfer for text-to-speech using data augmentation
Manuel Sam Ribeiro, Julian Roth, Giulia Comini +3
We address the problem of cross-speaker style transfer for text-to-speech (TTS) using data augmentation via voice conversion. We assume to have a corpus of neutral non-expressive d…
Enhancing audio quality for expressive Neural Text-to-Speech
Abdelhamid Ezzerg, Adam Gabrys, Bartosz Putrycz +7
Artificial speech synthesis has made a great leap in terms of naturalness as recent Text-to-Speech (TTS) systems are capable of producing speech with similar quality to human recor…
Voicy: Zero-Shot Non-Parallel Voice Conversion in Noisy Reverberant Environments
Alejandro Mottini, Jaime Lorenzo-Trueba, Sri Vishnu Kumar Karlapati +1
Voice Conversion (VC) is a technique that aims to transform the non-linguistic information of a source utterance to change the perceived identity of the speaker. While there is a r…
A learned conditional prior for the VAE acoustic space of a TTS system
Penny Karanasou, Sri Karlapati, Alexis Moinet +5
Many factors influence speech yielding different renditions of a given sentence. Generative models, such as variational autoencoders (VAEs), capture this variability and allow mult…
Weakly-supervised word-level pronunciation error detection in non-native English speech
Daniel Korzekwa, Jaime Lorenzo-Trueba, Thomas Drugman +2
We propose a weakly-supervised model for word-level mispronunciation detection in non-native (L2) English speech. To train this model, phonetically transcribed L2 speech is not req…
Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems
Shubhi Tyagi, Antonio Bonafonte, Jaime Lorenzo-Trueba +1
Developing Text Normalization (TN) systems for Text-to-Speech (TTS) on new languages is hard. We propose a novel architecture to facilitate it for multiple languages while using da…
Mispronunciation Detection in Non-native (L2) English with Uncertainty Modeling
Daniel Korzekwa, Jaime Lorenzo-Trueba, Szymon Zaporowski +3
A common approach to the automatic detection of mispronunciation in language learning is to recognize the phonemes produced by a student and compare it to the expected pronunciatio…
EmoCat: Language-agnostic Emotional Voice Conversion
Bastian Schnell, Goeric Huybrechts, Bartek Perz +2
Emotional voice conversion models adapt the emotion in speech without changing the speaker identity or linguistic content. They are less data hungry than text-to-speech models and…
Parallel WaveNet conditioned on VAE latent vectors
Jonas Rohnke, Tom Merritt, Jaime Lorenzo-Trueba +4
Recently the state-of-the-art text-to-speech synthesis systems have shifted to a two-model approach: a sequence-to-sequence model to predict a representation of speech (typically m…
Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention
Daniel Korzekwa, Roberto Barra-Chicote, Szymon Zaporowski +6
This paper describes two novel complementary techniques that improve the detection of lexical stress errors in non-native (L2) English speech: attention-based feature extraction an…
Low-resource expressive text-to-speech using data augmentation
Goeric Huybrechts, Thomas Merritt, Giulia Comini +3
While recent neural text-to-speech (TTS) systems perform remarkably well, they typically require a substantial amount of recordings from the target speaker reading in the desired s…
CAMP: a Two-Stage Approach to Modelling Prosody in Context
Zack Hodari, Alexis Moinet, Sri Karlapati +6
Prosody is an integral part of communication, but remains an open problem in state-of-the-art speech synthesis. There are two major issues faced when modelling prosody: (1) prosody…
Voice Conversion for Whispered Speech Synthesis
Marius Cotescu, Thomas Drugman, Goeric Huybrechts +2
We present an approach to synthesize whisper by applying a handcrafted signal processing recipe and Voice Conversion (VC) techniques to convert normally phonated speech to whispere…
Dynamic Prosody Generation for Speech Synthesis using Linguistics-Driven Acoustic Embedding Selection
Shubhi Tyagi, Marco Nicolis, Jonas Rohnke +2
Recent advances in Text-to-Speech (TTS) have improved quality and naturalness to near-human capabilities when considering isolated sentences. But something which is still lacking i…
Transformation of low-quality device-recorded speech to high-quality speech using improved SEGAN model
Seyyed Saeed Sarfjoo, Xin Wang, Gustav Eje Henter +3
Nowadays vast amounts of speech data are recorded from low-quality recorder devices such as smartphones, tablets, laptops, and medium-quality microphones. The objective of this res…