activity
20232026
most citedLearning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition

2 citations · 5 across the 9 of their papers we have counts for

collaborators

11 papers

cs.CV2026

LLM-based Multimodal Personality Recognition via Facial Action Unit-Text Semantic Fusion

Tianyi Zhang, Wei Shan, Yuan Zong +2

Personality recognition in asynchronous video interviews (AVIs) has become increasingly important due to their widespread adoption in modern recruitment. Existing approaches often…

eess.AS2026

AffectSpeech: A Large-Scale Emotional Speech Dataset with Fine-Grained Textual Descriptions for Speech Emotion Captioning and Synthesis

Tianhua Qi, Wenming Zheng, Björn W. Schuller +2

Emotion is essential in spoken communication, yet most existing frameworks in speech emotion modeling rely on predefined categories or low-dimensional continuous attributes, which…

eess.AS2024

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…

cs.CV20241 cited

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…

eess.AS2024

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…

cs.SD2024

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…