14 accepted papers
This paper introduces our solution, the Task-Specific Feature Learning (TSFL) method, designed to address the second track of the MEIJU Challenge at ICASSP 2025, namely, Imbalanced Emotion and Intent Recognition (English). The TSFL method incorporates three core components: the use of LLM features t…
This paper describes a Reliable Learning Framework (RLF) for the 1st Multimodal Emotion and Intent Joint Understanding (MEIJU) Challenge at ICASSP 2025. Our proposed RLF includes a Hierarchical Interaction Network and a Reliable Fusion Strategy. The former can excavate emotion and intent cues from t…
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 scenarios due to data privacy protection concerns. This p…
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 feature distributions of data from different speakers. Consequently, when the trained model is confronted with data from new s…
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 perception. To improve the content naturalness of con…
Micro-expression (ME) recognition is challenging due to the low-intensity facial motions. An idea to overcome this is learning assisted by macro-expressions (MaEs). However, the intensity gap between MaE and ME is so huge that related works fail to effectively leverage MaE’s assistance in overcoming…
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. g., word, phrase, and utterance. Drawing above in…
Micro-Expression Recognition (MER) is challenging because the Micro-Expressions' (ME) motion is too weak to distinguish. This hurdle can be tackled by enhancing intensity for a more accurate acquisition of movements. However, existing magnification strategies tend to use the features of facial image…
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 testing (target) speech signals come from different co…
Recently, Transformer-based architecture has been introduced into face super-resolution task due to its advantage in capturing long-range dependencies. However, these approaches tend to integrate global information in a large searching region, which neglect to focus on the most relevant information…
Micro-Expression recognition (MER) is a challenging task due to the short duration and low intensity of Micro-Expressions. A popular method to tackle this is magnifying MEs so as to enlarge the expression intensity to make recognition easier. However, the single fixed magnification strategy, widely…
In this paper, we focus on the research of cross-corpus speech emotion recognition (SER), in which the training and testing speech signals in cross-corpus SER belong to dierent speech corpus. Due to this fact, mismatched feature distributions may exist between the training and testing speech feature…
Unsupervised cross-database facial expression recognition (FER) is a challenging problem, in which the training and testing samples belong to different facial expression databases. For this reason, the training (source) and testing (target) facial expression samples would have different feature dist…
In this paper, we investigate an interesting problem, i.e., unsupervised cross-corpus speech emotion recognition (SER), in which the training and testing speech signals come from two different speech emotion corpora. Meanwhile, the training speech signals are labeled, while the label information of…