2 citations · 7 across the 12 of their papers we have counts for
10 papers · 1 filter
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…
Automated detection of pronunciation errors in non-native English speech employing deep learning
Daniel Korzekwa
Despite significant advances in recent years, the existing Computer-Assisted Pronunciation Training (CAPT) methods detect pronunciation errors with a relatively low accuracy (preci…
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…
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…
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…