The paper introduces a multimodal emotion recognition framework that combines audio and visual feature extraction with an attention-based fusion strategy. Audio features include Wav2Vec2 embeddings, MFCCs, and statistical acoustic descriptors, fused via a BiLSTM, while video features are extracted using a ResNet50-BiLSTM architecture. A multi-head attention mechanism fuses these modalities, and experiments on MELD and IEMOCAP show significant accuracy and robustness gains, especially in unbalanced data settings.
By Xu Lin, Ke Wang, Hui Kang, Xinying Wang
arXiv:2609.39453v1 Announce Type: cross
Abstract: Speech emotion recognition (SER) is the task of assigning emotion labels to utterances. Early systems relied on acoustic features, whereas recent app...
By Hezhao Zhang, Thomas Hain
arXiv:2607. 14702v1 Announce Type: cross Abstract: Automatic recognition of ambivalence and hesitancy is challenging because these states may be expressed through inconsistent linguistic, acoustic, facial, and contextual patterns, while top-performing systems often rely on computationally expensive ensembles.
By Elena Ryumina (St. Petersburg Federal Research Center of the Russian Academy of Sciences), Maxim Markitantov (St. Petersburg Federal Research Center of the Russian Academy of Sciences), Alexandr Axyonov (St. Petersburg Federal Research Center of the Russian Academy of Sciences), Fedor Shchetinin (HSE University, St. Petersburg, Russia), Timur Abdulkadirov (St. Petersburg Federal Research Center of the Russian Academy of Sciences), Dmitry Ryumin (St. Petersburg Federal Research Center of the Russian Academy of Sciences), Alexey Karpov (St. Petersburg Federal Research Center of the Russian Academy of Sciences)
arXiv:2608.20905v1 Announce Type: new
Abstract: Face-to-face audiovisual interaction is central to human communication, conveying rich emotional and social cues. However, existing multimodal dialogue...
By Yi Zheng, Yifan Xu, Yan Zhou, Hejia Chen, Chunyu Qiang, Xiaoqiang Liu, Xiaohan Li, Shenze Huang, Yue Zhang, Guoying Zhao, Pengfei Wan
arXiv:2607. 06611v1 Announce Type: cross Abstract: Automatically recognizing the sentiment, positive or negative, from speech is a challenging task, requiring both the analysis of vocal inflections and the interpretation of uttered words.
By Andrei-George Durdun, Victor Constantinescu, Radu Tudor Ionescu
The paper introduces an LLM-based framework for continuous dimensional emotion evaluation in multimodal dialogue, combining discrete emotion recognition with Valence-Arousal-Dominance (VAD) assessment on the IEMOCAP dataset. It incorporates acoustic cues as natural language descriptions via the SpeechCueLLM approach and evaluates six models from the LLaMA, GPT, and Qwen families using zero-shot, few-shot, and LoRA fine-tuning. LoRA-fine-tuned LLaMA models outperform prompt-engineered GPT models, achieving a new state-of-the-art Valence CCC of 0.7822, and ablation studies show that textual audio descriptions significantly benefit smaller models.
"whyItMatters":"The study demonstrates that domain adaptation through fine-tuning can surpass larger GPT models in multimodal emotion evaluation, highlighting the importance of tailored training for emotion recognition tasks."
By Yutong Hu, Jinho Choi
arXiv:2609.09924v1 Announce Type: new
Abstract: Emotion Recognition in Conversations (ERC) requires integrating heterogeneous textual, audio, and visual cues while accounting for conversational conte...
By Oriol Mar\'in, Roger Mar\'i, Gloria Haro, Rafael Redondo
arXiv:2609.06188v1 Announce Type: new
Abstract: Multimodal sentiment analysis and emotion recognition in conversations demand effective modeling of heterogeneous interactions across textual, acoustic...
By Pengfei Shao, Jisheng Dang, Jiawen Fang, Ning Liu, Wencan Zhang, Bimei Wang, Jingwen Zhao, Jianhuang Lai, Qi Tian, Tat-Seng Chua
arXiv:2512.07571v3 Announce Type: replace
Abstract: This paper presents a simple method that allows to easily enhance textual pre-trained large language models with speech information, when fine-tune...
By Nicolas Calbucura, Jose Guillen, Valentin Barriere
arXiv:2606. 02679v1 Announce Type: new Abstract: Multimodal systems often benefit from combining information across language, sound, and visual streams, but this benefit is not guaranteed.
By Jiyuan Liu, Liangwei Nathan Zheng, Wei Emma Zhang, Xinpei Wang, Weitong Chen
BiCFlow-MER introduces a conditional-flow framework for audio-text multimodal emotion recognition, treating the task as generative evidence transport within a structured emotion space. It disentangles emotion-oriented evidence from speaker style and lexical content, creating a conflict-aware affective condition that guides bidirectional rectified flow to an explicit emotion-space endpoint. The model verifies candidate emotions via adaptive prototype-cloud scoring and backward class-to-condition consistency, achieving superior performance on IEMOCAP, MELD, and the zero-shot CASE benchmark.
By Yanbing Wang, Shenyue Wang, Chunyang Yu
In multimodal emotion recognition (MER), human affective states are inferred by integrating complementary cues from multiple modalities. In audio-text MER, affective cues are often entangled with spea...