6 citations · 15 across the 9 of their papers we have counts for
9 papers
LikeBench: Evaluating Subjective Likability in LLMs for Personalization
Md Awsafur Rahman, Adam Gabrys, Doug Kang +3
A personalized LLM should remember user facts, apply them correctly, and adapt over time to provide responses that the user prefers. Existing LLM personalization benchmarks are lar…
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 (…
Del Visual al Auditivo: Sonorización de Escenas Guiada por Imagen
María Sánchez, Laura Fernández, Julián Arias +6
Recent advances in image, video, text and audio generative techniques, and their use by the general public, are leading to new forms of content generation. Usually, each modality w…
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…
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…
Universal Neural Vocoding with Parallel WaveNet
Yunlong Jiao, Adam Gabrys, Georgi Tinchev +3
We present a universal neural vocoder based on Parallel WaveNet, with an additional conditioning network called Audio Encoder. Our universal vocoder offers real-time high-quality s…
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…