1 citations · 2 across the 6 of their papers we have counts for
6 papers
Temporal Label Hierachical Network for Compound Emotion Recognition
Sunan Li, Hailun Lian, Cheng Lu +5
The emotion recognition has attracted more attention in recent decades. Although significant progress has been made in the recognition technology of the seven basic emotions, exist…
PAVITS: Exploring Prosody-aware VITS for End-to-End Emotional Voice Conversion
Tianhua Qi, Wenming Zheng, Cheng Lu +2
In this paper, we propose Prosody-aware VITS (PAVITS) for emotional voice conversion (EVC), aiming to achieve two major objectives of EVC: high content naturalness and high emotion…
Improving Speaker-independent Speech Emotion Recognition Using Dynamic Joint Distribution Adaptation
Cheng Lu, Yuan Zong, Hailun Lian +3
In speaker-independent speech emotion recognition, the training and testing samples are collected from diverse speakers, leading to a multi-domain shift challenge across the featur…
Layer-Adapted Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition
Yan Zhao, Yuan Zong, Jincen Wang +4
In this paper, we propose a new unsupervised domain adaptation (DA) method called layer-adapted implicit distribution alignment networks (LIDAN) to address the challenge of cross-c…
Learning Local to Global Feature Aggregation for Speech Emotion Recognition
Cheng Lu, Hailun Lian, Wenming Zheng +3
Transformer has emerged in speech emotion recognition (SER) at present. However, its equal patch division not only damages frequency information but also ignores local emotion corr…
Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition
Yan Zhao, Jincen Wang, Yuan Zong +3
In this paper, we propose a novel deep transfer learning method called deep implicit distribution alignment networks (DIDAN) to deal with cross-corpus speech emotion recognition (S…