Zobrazeno 1 - 10
of 466
pro vyhledávání: '"Zheng, Wenming"'
With the advancement of artificial intelligence (AI) technology, group-level emotion recognition (GER) has emerged as an important area in analyzing human behavior. Early GER methods are primarily relied on handcrafted features. However, with the pro
Externí odkaz:
http://arxiv.org/abs/2408.15276
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 propose Emotional Intensity-aware Network (EINet), dynamically adjusting int
Externí odkaz:
http://arxiv.org/abs/2407.14800
Autor:
Li, Sunan, Lian, Hailun, Lu, Cheng, Zhao, Yan, Qi, Tianhua, Yang, Hao, Zong, Yuan, Zheng, Wenming
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, existing methods are still hard to tackle compound emotion recognition that
Externí odkaz:
http://arxiv.org/abs/2407.12973
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 emotional naturalness, which are crucial for meeting the demands of human per
Externí odkaz:
http://arxiv.org/abs/2403.01494
Emotion-Aware Contrastive Adaptation Network for Source-Free Cross-Corpus Speech Emotion Recognition
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 source data during adaptation, which is unattainable in real-life scenari
Externí odkaz:
http://arxiv.org/abs/2401.12925
Swin-Transformer has demonstrated remarkable success in computer vision by leveraging its hierarchical feature representation based on Transformer. In speech signals, emotional information is distributed across different scales of speech features, e.
Externí odkaz:
http://arxiv.org/abs/2401.10536
Cross-corpus speech emotion recognition (SER) poses a challenge due to feature distribution mismatch, potentially degrading the performance of established SER methods. In this paper, we tackle this challenge by proposing a novel transfer subspace lea
Externí odkaz:
http://arxiv.org/abs/2312.06466
Micro-expression recognition (MER) in low-resolution (LR) scenarios presents an important and complex challenge, particularly for practical applications such as group MER in crowded environments. Despite considerable advancements in super-resolution
Externí odkaz:
http://arxiv.org/abs/2310.10022
This paper focuses on the research of micro-expression recognition (MER) and proposes a flexible and reliable deep learning method called learning to rank onset-occurring-offset representations (LTR3O). The LTR3O method introduces a dynamic and reduc
Externí odkaz:
http://arxiv.org/abs/2310.04664
Group-level emotion recognition (GER) is an inseparable part of human behavior analysis, aiming to recognize an overall emotion in a multi-person scene. However, the existing methods are devoted to combing diverse emotion cues while ignoring the inhe
Externí odkaz:
http://arxiv.org/abs/2310.04306