HumDial-EIBench is a new benchmark using real human dialogues to evaluate audio language models on emotional intelligence tasks including multi-turn tracking, causal reasoning, empathy generation, and acoustic-semantic conflict resolution.
When tone and words disagree: Towards robust speech emotion recognition un- der acoustic-semantic conflict
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A survey of Large Audio Language Models that establishes a taxonomy of trustworthiness vulnerabilities and proposes a Defense-in-Depth roadmap for audio intelligence.
CogAudio-LLM introduces LIME-440K dataset, EIPS chain-of-thought reasoning, and DR-SAPO optimization to address semantic dominance and improve affective responses in audio language models.
citing papers explorer
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HumDial-EIBench: A Human-Recorded Multi-Turn Emotional Intelligence Benchmark for Audio Language Models
HumDial-EIBench is a new benchmark using real human dialogues to evaluate audio language models on emotional intelligence tasks including multi-turn tracking, causal reasoning, empathy generation, and acoustic-semantic conflict resolution.
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A Survey of Large Audio Language Models: Generalization, Trustworthiness, and Outlook
A survey of Large Audio Language Models that establishes a taxonomy of trustworthiness vulnerabilities and proposes a Defense-in-Depth roadmap for audio intelligence.
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Beyond Semantic Dominance: Cognitive Affective Reasoning and Empathetic Response Alignment in Audio Language Models
CogAudio-LLM introduces LIME-440K dataset, EIPS chain-of-thought reasoning, and DR-SAPO optimization to address semantic dominance and improve affective responses in audio language models.