Releases body-pose dataset for communicative intent recognition and introduces consistency-based reliability measure with a proof bounding its accuracy probability for real-time robot applications.
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MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
14 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Emotion recognition in conversations is a challenging task that has recently gained popularity due to its potential applications. Until now, however, a large-scale multimodal multi-party emotional conversational database containing more than two speakers per dialogue was missing. Thus, we propose the Multimodal EmotionLines Dataset (MELD), an extension and enhancement of EmotionLines. MELD contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends. Each utterance is annotated with emotion and sentiment labels, and encompasses audio, visual and textual modalities. We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations. The full dataset is available for use at http:// affective-meld.github.io.
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SIV-Bench is a new video benchmark with 2,792 clips and 5,455 QA pairs that evaluates MLLMs on social scene understanding, state reasoning, and dynamics prediction using social relation theory.
This survey provides the first comprehensive overview of deep multimodal learning methods designed to remain robust when some input modalities are absent.
VITA-QinYu is the first expressive end-to-end spoken language model supporting role-playing and singing alongside conversation, trained on 15.8K hours of data and outperforming prior models on expressiveness and conversational benchmarks.
C-MET transfers emotions from speech to facial video by learning cross-modal semantic vectors with pretrained audio and disentangled expression encoders, yielding 14% higher emotion accuracy on MEAD and CREMA-D even for unseen emotions.
A new dataset and benchmark maps movie clips to distributions of audience emotional reactions derived from YouTube comments, showing that finetuned vision-language models can predict these distributions from video alone.
StableToken introduces a multi-branch architecture with bit-wise voting to create noise-robust semantic speech tokens, achieving lower Unit Edit Distance and better SpeechLLM robustness than prior single-path tokenizers.
Gaslighting attacks using Anger, Cognitive Disruption, Sarcasm, Implicit, and Professional Negation strategies cause a 24.3% average accuracy drop in Speech LLMs while also triggering behavioral changes like apologies and refusals.
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MAJIC reports 93% accuracy and 91% F1 on emotion classification by fusing articulatory motion features with audio via multi-task learning, tested on 20 participants across 10 languages and prompted/conversational speech.
Kimi-Audio is an open-source audio foundation model that achieves state-of-the-art results on speech recognition, audio understanding, question answering, and conversation after pre-training on more than 13 million hours of speech, sound, and music data.
Proposes cross-attention audio-video fusion and VE-MD latent-space models for group emotion recognition that avoid individual cues and report competitive performance via ablation studies on synthetic and real data.
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