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@article{TAC_2026,

title = {Improving Emotion Recognition from Ambiguous Speech via Spatio-Temporal Spectrum Analysis and Real-Time Soft-Label Correction},

author = {Chenquan Gan and Daitao Zhou and Qingyi Zhu and Xibin Wang and Deepak Kumar Jain and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2025/12/Manuscript_clean.pdf},

year  = {2026},

date = {2026-03-01},

urldate = {2026-03-01},

journal = {IEEE Transactions on Affective Computing},

pages = {1-16},

abstract = {Speech represents a fundamental medium for conveying human emotions and, as a result, speech-based emotion recognition (SER) systems have become pivotal in advancing human-computer interaction (HCI) across a range of applications. While significant progress has been made in speech emotion recognition over recent years, existing solutions still face several key challenges, in that they: (i)  rely excessively  on subjectively annotated (discrete) labels during training, (ii)  often overlook the label ambiguity of speech samples that express more than one class of emotions, and (iii)  underutilize unlabeled or ambiguous speech, for which typically a label distribution (or so-called soft labels) is available. To address these issues, we propose in this paper a novel SER model that explicitly handles ambiguous  speech samples and overcomes the shortcomings outlined above. Central to our approach is a novel real-time soft-label correction strategy designed to refine the annotations assigned to ambiguous speech. The proposed model leverages both, (explicitly) labeled as well as ambiguous samples and applies the dynamic soft-label correction strategy alongside an enhanced inter-class difference loss function to iteratively optimize the label distributions during training. We theoretically demonstrate that our method is capable of approximating the true emotional distribution of speech even in the presence of label noise, suggesting that utilizing ambiguous speech samples without explicit emotion labels still contributes toward more effective emotion recognition. Furthermore, we integrate the representational power of convolutional neural networks (CNNs) with the contextual modeling capabilities of Wav2Vec 2.0 to enable a comprehensive extraction of spatio-temporal speech features. Experimental results on the IEMOCAP multi-label dataset confirm the effectiveness of our approach, achieving state-of-the-art performance with significant improvements in weighted accuracy (WA) and unweighted accuracy (UA) over competing methods.},

keywords = {deep learning, emotion recognition, speech, speech processing},

pubstate = {published},

tppubtype = {article}

}

@article{CVIU_2025b,

title = {Optimizing ambiguous speech emotion recognition through spatial–temporal parallel network with label correction strategy},

author = {Chenquan Gan and Daitao Zhou and Kexin Wang and Qingyi Zhu and Deepak Kumar Jain and Vitomir Štruc},

url = {https://www.sciencedirect.com/science/article/pii/S1077314225002061?dgcid=coauthor

https://lmi.fe.uni-lj.si/wp-content/uploads/2025/09/CVIU.pdf},

doi = {https://doi.org/10.1016/j.cviu.2025.104483},

year  = {2025},

date = {2025-10-01},

urldate = {2025-10-01},

journal = {Computer Vision and Image Understanding},

volume = {260},

number = { 104483},

pages = {1--14},

abstract = {Speech emotion recognition is of great significance for improving the human–computer interaction experience. However, traditional methods based on hard labels have difficulty dealing with the ambiguity of emotional expression. Existing studies alleviate this problem by redefining labels, but still rely on the subjective emotional expression of annotators and fail to consider the truly ambiguous speech samples without dominant labels fully. To solve the problems of insufficient expression of emotional labels and ignoring ambiguous undominantly labeled speech samples, we propose a label correction strategy that uses a model with exact sample knowledge to modify inappropriate labels for ambiguous speech samples, integrating model training with emotion cognition, and considering the ambiguity without dominant label samples. It is implemented on a spatial–temporal parallel network, which adopts a temporal pyramid pooling (TPP) to process the variable-length features of speech to improve the recognition efficiency of speech emotion. Through experiments, it has been shown that ambiguous speech after label correction has a more promoting effect on the recognition performance of speech emotions.},

keywords = {deep learning, emotion recognition, speech, speech processing, speech technologies},

pubstate = {published},

tppubtype = {article}

}

@article{Inform-Gajsek_2009,

title = {Multi-modal emotional database: AvID},

author = {Rok Gajšek and Vitomir Štruc and France Mihelič and Anja Podlesek and Luka Komidar and Gregor Sočan and Boštjan Bajec},

url = {https://lmi.fe.uni-lj.si/en/multi-modalemotionaldatabaseavid/},

year  = {2009},

date = {2009-01-01},

urldate = {2009-01-01},

journal = {Informatica (Ljubljana)},

volume = {33},

number = {1},

pages = {101-106},

abstract = {This paper presents our work on recording a multi-modal database containing emotional audio and video recordings. In designing the recording strategies a special attention was payed to gather data involving spontaneous emotions and therefore obtain a more realistic training and testing conditions for experiments. With specially planned scenarios including playing computer games and conducting an adaptive intelligence test different levels of arousal were induced. This will enable us to both detect different emotional states as well as experiment in speaker identification/verification of people involved in communications. So far the multi-modal database has been recorded and basic evaluation of the data was processed.},

keywords = {avid, database, dataset, emotion recognition, facial expression recognition, speech, speech technologies, spontaneous emotions},

pubstate = {published},

tppubtype = {article}

}

@article{CVIU_2025,

title = {Optimizing ambiguous speech emotion recognition through spatial–temporal parallel network with label correction strategy},

author = {Chenquan Gan and Daitao Zhou and Kexin Wang and Qingyi Zhu and Deepak Kumar Jain and Vitomir Štruc},

url = {https://www.sciencedirect.com/science/article/pii/S1077314225002061?dgcid=coauthor

https://lmi.fe.uni-lj.si/wp-content/uploads/2025/09/CVIU.pdf},

doi = {https://doi.org/10.1016/j.cviu.2025.104483},

journal = {Computer Vision and Image Understanding},

volume = {260},

number = {104483},

pages = {1--14},

abstract = {Speech emotion recognition is of great significance for improving the human–computer interaction experience. However, traditional methods based on hard labels have difficulty dealing with the ambiguity of emotional expression. Existing studies alleviate this problem by redefining labels, but still rely on the subjective emotional expression of annotators and fail to consider the truly ambiguous speech samples without dominant labels fully. To solve the problems of insufficient expression of emotional labels and ignoring ambiguous undominantly labeled speech samples, we propose a label correction strategy that uses a model with exact sample knowledge to modify inappropriate labels for ambiguous speech samples, integrating model training with emotion cognition, and considering the ambiguity without dominant label samples. It is implemented on a spatial–temporal parallel network, which adopts a temporal pyramid pooling (TPP) to process the variable-length features of speech to improve the recognition efficiency of speech emotion. Through experiments, it has been shown that ambiguous speech after label correction has a more promoting effect on the recognition performance of speech emotions.},

keywords = {deep learning, emotion recognition, speech, speech processing, speech technologies},

pubstate = {published},

tppubtype = {article}

}