7 citations · 8 across the 6 of their papers we have counts for
6 papers
Emotion-Aware Contrastive Adaptation Network for Source-Free Cross-Corpus Speech Emotion Recognition
Yan Zhao, Jincen Wang, Cheng Lu +4
Cross-corpus speech emotion recognition (SER) aims to transfer emotional knowledge from a labeled source corpus to an unlabeled corpus. However, prior methods require access to sou…
Speech Swin-Transformer: Exploring a Hierarchical Transformer with Shifted Windows for Speech Emotion Recognition
Yong Wang, Cheng Lu, Hailun Lian +4
Swin-Transformer has demonstrated remarkable success in computer vision by leveraging its hierarchical feature representation based on Transformer. In speech signals, emotional inf…
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…
Uncertainty Quantification for Traffic Forecasting: A Unified Approach
Weizhu Qian, Dalin Zhang, Yan Zhao +2
Uncertainty is an essential consideration for time series forecasting tasks. In this work, we specifically focus on quantifying the uncertainty of traffic forecasting. To achieve t…