8 citations · 13 across the 11 of their papers we have counts for
11 papers
Towards Realistic Emotional Voice Conversion using Controllable Emotional Intensity
Tianhua Qi, Shiyan Wang, Cheng Lu +3
Realistic emotional voice conversion (EVC) aims to enhance emotional diversity of converted audios, making the synthesized voices more authentic and natural. To this end, we propos…
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…
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…
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…
Towards Domain-Specific Cross-Corpus Speech Emotion Recognition Approach
Yan Zhao, Yuan Zong, Hailun Lian +3
Cross-corpus speech emotion recognition (SER) poses a challenge due to feature distribution mismatch, potentially degrading the performance of established SER methods. In this pape…
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…
Time-Frequency Transformer: A Novel Time Frequency Joint Learning Method for Speech Emotion Recognition
Yong Wang, Cheng Lu, Yuan Zong +3
In this paper, we propose a novel time-frequency joint learning method for speech emotion recognition, called Time-Frequency Transformer. Its advantage is that the Time-Frequency T…
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…
Speech Emotion Recognition via an Attentive Time-Frequency Neural Network
Cheng Lu, Wenming Zheng, Hailun Lian +4
Spectrogram is commonly used as the input feature of deep neural networks to learn the high(er)-level time-frequency pattern of speech signal for speech emotion recognition (SER).…