6 citations · 6 across the 6 of their papers we have counts for
6 papers
Automatic Speech Disentanglement for Voice Conversion using Rank Module and Speech Augmentation
Zhonghua Liu, Shijun Wang, Ning Chen
Voice Conversion (VC) converts the voice of a source speech to that of a target while maintaining the source's content. Speech can be mainly decomposed into four components: conten…
Learning Emotional Representations from Imbalanced Speech Data for Speech Emotion Recognition and Emotional Text-to-Speech
Shijun Wang, Jón Guðnason, Damian Borth
Effective speech emotional representations play a key role in Speech Emotion Recognition (SER) and Emotional Text-To-Speech (TTS) tasks. However, emotional speech samples are more…
Fine-grained Emotional Control of Text-To-Speech: Learning To Rank Inter- And Intra-Class Emotion Intensities
Shijun Wang, Jón Guðnason, Damian Borth
State-of-the-art Text-To-Speech (TTS) models are capable of producing high-quality speech. The generated speech, however, is usually neutral in emotional expression, whereas very o…
Generative Data Augmentation Guided by Triplet Loss for Speech Emotion Recognition
Shijun Wang, Hamed Hemati, Jón Guðnason +1
Speech Emotion Recognition (SER) is crucial for human-computer interaction but still remains a challenging problem because of two major obstacles: data scarcity and imbalance. Many…
Zero-shot Voice Conversion via Self-supervised Prosody Representation Learning
Shijun Wang, Dimche Kostadinov, Damian Borth
Voice Conversion (VC) for unseen speakers, also known as zero-shot VC, is an attractive research topic as it enables a range of applications like voice customizing, animation produ…
NoiseVC: Towards High Quality Zero-Shot Voice Conversion
Shijun Wang, Damian Borth
Voice conversion (VC) is a task that transforms voice from target audio to source without losing linguistic contents, it is challenging especially when source and target speakers a…