arXiv Computer Vision
EmotionDialogCN: A Spontaneous Multimodal Dataset for Mandarin Emotional Dialogue
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InCarEmo: A Multimodal Dataset for In-Cabin Emotion Recognition and Driver State Monitoring
arXiv:2607. 14683v1 Announce Type: new Abstract: Understanding driver emotion and state is critical for the next generation of intelligent in-cabin systems that ensure safety and enhance human-vehicle interaction.
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Jul 16
InCarEmo: A Multimodal Dataset for In-Cabin Emotion Recognition and Driver State Monitoring
Understanding driver emotion and state is critical for the next generation of intelligent in-cabin systems that ensure safety and enhance human-vehicle interaction. However, existing public datasets for in-cabin affective computing are largely limited to visual modalities and rarely include conversational information, making it difficult to capture the linguistic and interactive cues underlying driver emotion.
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AuEmoChat: Authentic Emotion Understanding and Rendering for Conversational Speech Synthesis
arXiv:2607. 15755v1 Announce Type: cross Abstract: Conversational Speech Synthesis (CSS) aims to synthesize speech with human-like emotional expression and contextual consistency in user-agent interactions.
arXiv Machine Learning
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Sympatheia: Emotionally Adaptive Voice Assistant with Continuous Affect Conditioning
arXiv:2606. 00851v1 Announce Type: cross Abstract: Empathetic spoken dialogue systems must infer a user's emotional state to respond appropriately, yet everyday speech often carries weak, neutral, or ambiguous affective cues.
arXiv AI
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Beyond the Mouth: Upper-Face Affective Cues in Audiovisual Sentence Recognition under Acoustic Uncertainty
arXiv:2606. 00670v1 Announce Type: cross Abstract: Face-to-face speech comprehension is inherently multimodal, integrating acoustic signals with visible articulation, facial expression, head motion, and other socially relevant cues.
arXiv Machine Learning
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EmoEUS: Uncertainty Supervision for Multimodal Emotion Recognition in Conversation
arXiv:2607. 18336v1 Announce Type: cross Abstract: Multimodal emotion recognition in conversation (MERC) can leverage multimodal and contextual cues to boost recognition performance.