10 citations · 13 across the 10 of their papers we have counts for
10 papers
Can Bayesian Optimization Efficiently Find a Strong Single Expert in Neural Thickets?
Nigel Bastian Cendra, Abdelhamid Ezzerg, Fernando Julio Cendra +2
Gradient-free post-training has emerged as a compelling alternative to gradient-based optimization for large language models (LLMs), but existing approaches remain costly. We ask w…
Robust Bayesian Optimisation with Unbounded Corruptions
Abdelhamid Ezzerg, Ilija Bogunovic, Jeremias Knoblauch
Bayesian Optimization is critically vulnerable to extreme outliers. Existing provably robust methods typically assume a bounded cumulative corruption budget, which makes them defen…
AE-Flow: AutoEncoder Normalizing Flow
Jakub Mosiński, Piotr Biliński, Thomas Merritt +2
Recently normalizing flows have been gaining traction in text-to-speech (TTS) and voice conversion (VC) due to their state-of-the-art (SOTA) performance. Normalizing flows are unsu…
Creating New Voices using Normalizing Flows
Piotr Bilinski, Thomas Merritt, Abdelhamid Ezzerg +5
Creating realistic and natural-sounding synthetic speech remains a big challenge for voice identities unseen during training. As there is growing interest in synthesizing voices of…
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…
Remap, warp and attend: Non-parallel many-to-many accent conversion with Normalizing Flows
Abdelhamid Ezzerg, Thomas Merritt, Kayoko Yanagisawa +6
Regional accents of the same language affect not only how words are pronounced (i.e., phonetic content), but also impact prosodic aspects of speech such as speaking rate and intona…
GlowVC: Mel-spectrogram space disentangling model for language-independent text-free voice conversion
Magdalena Proszewska, Grzegorz Beringer, Daniel Sáez-Trigueros +3
In this paper, we propose GlowVC: a multilingual multi-speaker flow-based model for language-independent text-free voice conversion. We build on Glow-TTS, which provides an archite…
Text-free non-parallel many-to-many voice conversion using normalising flows
Thomas Merritt, Abdelhamid Ezzerg, Piotr Biliński +4
Non-parallel voice conversion (VC) is typically achieved using lossy representations of the source speech. However, ensuring only speaker identity information is dropped whilst all…
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…
Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speech
Raahil Shah, Kamil Pokora, Abdelhamid Ezzerg +5
Whilst recent neural text-to-speech (TTS) approaches produce high-quality speech, they typically require a large amount of recordings from the target speaker. In previous work, a 3…