Emotion and Mood Recognition

Últimos artículos

• EmoRES-TTS: Residual-Enhanced Vector Steering for Emotional Speech Generation

  Kuan-Po Huang, Haohe Liu, Puyuan Peng, Haibin Wu, Zhaoheng Ni, Hung-yi Lee, Jinwon Lee, Neha Chachra · 1 de octubre de 2026

  Emotion-conditioned text-to-speech (TTS) models may fail to express the requested emotion reliably, and improving controllability by additional training is costly in both computation and emotion-labeled speech training data. We therefore study vector steering, a training-free approach that modifies …

• Multimodal Detection of Higher-Order Behavioral Constructs: Self-Compassion in Structured Reflective Interaction

  Siddhant Jain, Dimitra Tsovaltzi · 1 de octubre de 2026

  Many of the qualities that matter most in how people learn and grow, how someone regulates their emotions, reflects on a setback, or stays aware of others during a difficult conversation, are not directly observable. They have to be inferred from how someone speaks, moves, and sounds over time, and …

• LongEmo: Towards Emotion Understanding and Reasoning in Long Videos

  Shuo Zhang, Yifan Zhou, Han Wang, Jinsong Zhang, Jingyu Li, Hongbing Li, Zhejun Zhang, Chengyi Zhao, Yuquan Hao, Yitong Liu, Jiyin Li, Ruiqi Tang, Zixuan Lin, Yi Luo, Xurui Zhang, Ronghao Chen, Huacan Wang, Lei Li · 1 de octubre de 2026

  While recent Multimodal Large Language Models (MLLMs) have shown promise in affective computing, their reasoning capabilities are largely confined to short video clips with limited interactions. However, real-world emotions are not merely isolated instantaneous reactions but dynamic and cumulative p…

• From Speech to Editable Concepts: Probing Emotion Recognition with Concept Bottleneck Models

  Hezhao Zhang, Thomas Hain · 1 de octubre de 2026

  Speech emotion recognition (SER) is the task of assigning emotion labels to utterances. Early systems relied on acoustic features, whereas recent approaches combine multiple modalities, most commonly speech and text. Still, performance remains poor on many datasets. Large language models (LLMs) have…

• Beyond Text: LLM-Based Dimensional Emotion Evaluation in Multimodal Dialogue

  Yutong Hu, Jinho Choi · 1 de octubre de 2026

  Emotion recognition in conversation has been widely studied, but applying Large Language Models (LLMs) to continuous dimensional emotion evaluation in multimodal dialogue remains largely unexplored. We propose an LLM-based framework that performs discrete emotion recognition and Valence-Arousal-Domi…

• VISTA: Value-Informed Event Appraisal for Multimodal Emotion Conflict

  Jiale Dai, Liuxian Ma, Xiaoke Niu, Wenjing Zhang, Huiying Zhao, Zhaoxiang Liu, Shiguo Lian, Guojie Song · 30 de septiembre de 2026

  Conflicting emotional cues can be individually valid: a subdued voice may reflect a blocked goal while a smile satisfies a social obligation. Their interpretation depends on what the event means to the person. We introduce VISTA (Value-Informed Semantic Trust Arbitration), a learned seven-field appr…

• Finding Emotions Where They Belong: Rethinking Audio Emotion Recognition through Masked Temporal Affective Grounding

  Abdelrahman Mohamed, Lars Kai Hansen, Zheng-Hua Tan · 29 de septiembre de 2026

  Audio emotion recognition (AER) typically assigns a single label to an entire recording, leaving the temporal scope of that label ambiguous when multiple speakers and affective events are present. We address this limitation by reformulating AER as a Temporal Affective Grounding (TAG) task that assoc…

• VoiceNet: Fine-Grained Voice Understanding Beyond Emotion at Scale

  Christoph Schuhmann, Robert Kaczmarczyk, Gollam Rabby, Felix Friedrich, Maurice Kraus, Gijs Wijngaard, Kourosh Nadi, Huu Nguyen, Kristian Kersting, S\"oren Auer · 29 de septiembre de 2026

  Expressive speech synthesis has outpaced expressive speech perception: systems now render fine-grained vocal performances that no public benchmark can score. Most benchmarks for this inverse problem stop at six to nine basic emotion categories, largely on acted speech. This paper introduces VoiceNet…

• Beyond End-to-End Black Box Mapping: An Intentional Agent Framework for Cognitive-driven Facial Reaction Generation

  Hanzhong Zhang, Jindong Wang, Siyang Song · 29 de septiembre de 2026

  Automatic human-like facial reaction generation (FRG) is essential for building intelligent systems that can engage in human-computer interaction (HCI). While diverse and context-appropriate facial reactions can reflect latent appraisal and affective processes in human interaction, most existing FRG…

• Differential Attention Unlocks Complementary EEG and Speech Fusion for Emotion Recognition

  Philip H. Lee, Shreeram Suresh Chandra, John H. L. Hansen · 28 de septiembre de 2026

  Multimodal emotion recognition (MER) increasingly pairs EEG with speech, treating internal neural signals and external vocal expression as informative views of affect. In practice, naive fusion underperforms the stronger single modality, because EEG artifacts inject noise that corrupts the shared re…

• Audio emotion recognition for atypical hearing

  Ulysse Roussel (STMS) · 28 de septiembre de 2026

  My doctoral work aims to explore Audio Emotion Recognition (AER) in the context of atypical listening. This research focuses on auditory hypersensitivity in people with autism, a phenomenon that is often difficult to evaluate and unique to each individual. Our core idea is to leverage our understand…

• Reliability-aware Cross-sample Enhancement for Robust Multimodal Sentiment Analysis

  Menghua Jiang, Haokai Gao, Xiangui Kang, Haifeng Hu, Sijie Mai · 28 de septiembre de 2026

  Multimodal Sentiment Analysis (MSA) aims to infer human emotions from multiple modalities such as text, audio, and vision. In practice, inputs are often corrupted by noise and missing modalities, which degrades performance. Existing methods typically address these challenges in isolation, limiting t…

• MoTop: Motion-Topological Model For Micro AU Detection

  Huai-Qian Khor, Mengting Wei, Yante Li, Chu Kiong Loo, Guoying Zhao · 28 de septiembre de 2026

  Facial micro-expressions are spontaneous, brief, and subtle facial movements that reveal suppressed emotions in high-stakes environments. In contrast to classic expression analysis, detecting action unit (AU) yields a finer representation of facial movements, serving as a preliminary step before def…

• UNWIND: Any-Length Facial Video for Stress Detection without Temporal Windowing

  Stefanos Gkikas, Christian Arzate Cruz, Eric Nichols, Giorgos Giannakakis, Randy Gomez · 25 de septiembre de 2026

  Automatic stress recognition from facial video provides a non-contact approach for affective monitoring. However, most existing video-based methods divide complete recordings into shorter temporal segments before performing classification. Such segmentation requires additional decisions concerning s…

• Cross-Modal Emotion Understanding: A Transformer-GAT Approach for Dialogue Emotion Recognition

  Jiaqi Qiao, Yifan Lyu, Xiujuan Xu · 25 de septiembre de 2026

  Multimodal emotion recognition is a key research area in affective computing, with applications in sentiment analysis, intelligent customer service, and human-computer interaction. However, existing methods often rely on single-modal features or simple multimodal fusion, failing to capture the syner…

• Enriching Speech Emotion Representations with Conversational Context

  Arthur Peuvot, Romaric Besan\c{c}on, Ga\"el de Chalendar, Bianca Vieru, Ioana Vasilescu · 23 de septiembre de 2026

  Detecting emotions is necessary for building systems that can accurately and adaptively interact with humans. Speech Emotion Recognition (SER) has become an important research focus to develop intelligent spoken interfaces. However, most studies predict emotions at the utterance level, ignoring the …

• Benchmarking Open-Source Speech Emotion Recognition in Naturalistic Mandarin Spine Clinic Consultations: A Pilot Validation Study

  Tsz Yuet Yeung, Zonglin He, Dong Chen, Huili Peng, Huiren Tao, Kenneth MC Cheung · 23 de septiembre de 2026

  Speech emotion recognition (SER) may enable passive affect monitoring in clinical encounters, but most systems are validated on acted laboratory speech rather than naturalistic Mandarin outpatient consultations. We benchmarked three open-source SER models (emotion2vec+, SenseVoice, FunASR) against a…

• Beyond the Raw Waveform: Fusing Visual Representations of EDA for Stress Detection

  Stefanos Gkikas, Thomas Kassiotis, Yang Guo, Guangliang Li, Eric Nichols, Houshyar Asadi, Nikolaos Smyrnis, Giorgos Giannakakis · 22 de septiembre de 2026

  Electrodermal activity (EDA) is widely used in automatic stress detection, yet most pipelines treat it only as a raw one-dimensional waveform. This study examines whether complementary visual representations of EDA provide useful information for stress classification and whether their fusion im- pro…

• Beyond Emotion Prompts: Fine-Grained Text-to-Image Generation Driven by Valence-Arousal-Dominance

  Minglang Li, Yueyue Fang, Xieping Gao · 22 de septiembre de 2026

  Although text-to-image models can accurately depict subjects and scenes, creators still struggle to specify the fine-grained emotions an image should convey without rewriting its content description. Natural language can suggest emotions, but it offers no control scale with stable meanings and order…

• Replicating the Geometry of Emotion Representations in a Base Open-Weights Model

  Adam Hollowell · 22 de septiembre de 2026

  Sofroniew et al. (2026) report that emotion concepts in Claude Sonnet 4.5 are represented as vectors whose geometry mirrors human affect psychology. We replicate the representational core of that study on the base pretrained model google/gemma-2-27b, inheriting every disclosed parameter, resolving u…

• Functional Emotion Without Character: Large Language Models, Aristotelian Disposition, and the Limits of Behavioral Alignment

  Marzieh Zare · 22 de septiembre de 2026

  Debates about whether artificial systems can feel are often forced between two unsatisfactory positions: behavioral equivalence is treated as sufficient for emotion, or phenomenal consciousness is treated as a prerequisite that makes the question empirically inaccessible. This article develops a str…

• Toward individual-level calibration in affect recognition with perceptual adjustment queries

  Xuanzhou Chen, Sankaraleengam Alagapan, Ashwin Pananjady · 21 de septiembre de 2026

  Behavioral tasks measuring facial affect perception assume that identical stimuli impose equivalent perceptual difficulty across participants. However, this assumption is systematically violated by individual differences in perceptual sensitivity. Using an affective perception task as our testbed, w…

• From Stress to Affect: Multimodal Deep Learning for Physiological Emotion Recognition Across Wearable Sensor Modalities

  Desta Haileselassie Hagos, Saurav Keshari Aryal, Legand L. Burge · 21 de septiembre de 2026

  Physiological emotion recognition using wearable sensors has important applications in mental health monitoring, affective computing, and human-computer interaction. However, existing studies typically evaluate a single model, sensing configuration, or dataset, limiting our understanding of how thes…

• Learned Parametric Emotion Editing: Real-Time Affective Filtering for On-Device Social Media Video

  Musa Rochi, Marcel Schubert, Christoph Gebhardt · 21 de septiembre de 2026

  Problematic internet use affects a growing share of the population, yet common interventions, e.g., time limits, blocking, forced breaks, are coercive and easily circumvented. We explore a less restrictive alternative: adapting the emotional intensity of visual content. Prior work has shown that opt…

• Modality Discrepancy Transformer for Ambivalence and Hesitancy Recognition

  Shiyu Luo, Yu Wang, Jiawen Huang, Zhaoxiang Xiao, Chenxi Huang, Qi Zhang, Bin Liu · 18 de septiembre de 2026

  Ambivalence and hesitancy (A/H) are affective states in which individuals express contradictory signals across facial, vocal, and linguistic channels. Automatically recognising A/H in clinical videos requires detecting cross-modal disagreement -- the signal that standard fusion methods suppress. Bas…