Výsledky vyhledávání - "Wang, Jincen"

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Emotion-Aware Contrastive Adaptation Network for Source-Free Cross-Corpus Speech Emotion Recognition

Autor: Zhao, Yan, Wang, Jincen, Lu, Cheng, Li, Sunan, Schuller, Björn, Zong, Yuan, Zheng, Wenming

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

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Layer-Adapted Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition

Autor: Zhao, Yan, Zong, Yuan, Wang, Jincen, Lian, Hailun, Lu, Cheng, Zhao, Li, Zheng, Wenming

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-corpus speech emotion recognition (SER). LIDAN extends our previous ICA

Externí odkaz: http://arxiv.org/abs/2310.03992

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Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition

Autor: Zhao, Yan, Wang, Jincen, Zong, Yuan, Zheng, Wenming, Lian, Hailun, Zhao, Li

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 (SER) problem, in which the labeled training (source) and unlabeled test

Externí odkaz: http://arxiv.org/abs/2302.08921

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