21 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…
Zero-shot Emotional Voice Conversion (EVC) aims to transform a speaker’s emotional state to match a target emotion, even for speakers and emotion categories that were not encountered during training, thereby enhancing the generalization ability of traditional EVC systems. Despite advancements in the…
Leveraging multimodal brain signals, such as electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS), for the objective detection of brain activity is regarded as a promising approach for affective brain-computer interface. Existing EEG-fNIRS bimodal methods primarily focus on d…
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…
Remote photoplethysmography (rPPG) holds great potential in medical surveillance. However, head movements commonly encountered in real-world scenarios often degrade physiological estimation performance, particularly for unsupervised learning methods based on physiological frequency band priors, whic…
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…
As an important research direction in the field of speech signal processing, cross-domain speech emotion recognition (SER) has attracted extensive attention. In practice, it is challenging to collect enough labeled samples from single source domain to train robust classifiers. To this end, this pape…
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…
In this paper, we propose a novel transfer learning framework, named generalized subspace distribution adaptation (GSDA), to tackle the challenging cross-corpus speech emotion recognition problem. First, we learn a common low-dimensional feature subspace by utilizing a generalized subspace learning…
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…
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…
Deep learning methods have been widely applied to automatic facial action unit (AU) intensity estimation and achieved state-of-the-art performance. These methods, however, are mostly appearance-based and fail to exploit the underlying structural information among the AUs. In this paper, we propose a…
Message passing neural network has been an effective method to represent dependencies among nodes by propagating messages. However, most of message passing algorithms focus on one structure and the messages are estimated by one single approach. For the real-world data, like facial action units (AUs)…
Capturing the dependencies among different facial action units (AU) is extremely important for the AU detection task. Many studies have employed graph-based deep learning methods to exploit the dependencies among AUs. However, the dependencies among AUs in real world data are often noisy and the unc…
In practical situations, the emotional speech utterances are often collected from different devices and conditions, which will obviously affect the recognition performance. To address this issue, in this paper, a novel transfer non-negative matrix factorization (TNMF) method is presented for cross-c…
In this paper, color facial expression recognition based on color local features is investigated, in which each color facial image is decomposed into three color component images. For each color component image, we extract a set of color local features to represent the color component image, where c…