HumDial-EIBench: 一个用于音频语言模型的情感智能基准测试
HumDial-EIBench: A Human-Recorded Multi-Turn Emotional Intelligence Benchmark for Audio Language Models
AI总结：
本文提出HumDial-EIBench，通过真实人类对话评估音频语言模型的情感智能，采用多选题和对抗性干扰项减少主观评分偏差，发现大多数模型在多轮情感跟踪和隐含因果推理上表现不佳。
摘要：
评估音频语言模型的情感智能至关重要。然而，现有基准测试大多依赖合成语音，仅限于单轮交互，并严重依赖开放式评分。本文提出了HumDial-EIBench，一个全面的基准测试，用于评估音频语言模型的情感智能。使用ICASSP 2026 HumDial挑战中的人类录制对话，将其重新表述为包含对抗性干扰项的多项选择题，以减轻认知任务中的主观评分偏差。它保留了生成共情回应的生成过程，并引入了音学-语义冲突任务以评估对矛盾多模态信号的鲁棒性。对八种音频语言模型的评估表明，大多数模型在多轮情感跟踪和隐含因果推理上存在困难。此外，所有模型都表现出文本和音学共情的分离，以及在跨模态冲突中的严重文本主导偏差。
英文摘要：
Evaluating the emotional intelligence (EI) of audio language models (ALMs) is critical. However, existing benchmarks mostly rely on synthesized speech, are limited to single-turn interactions, and depend heavily on open-ended scoring. This paper proposes HumDial-EIBench, a comprehensive benchmark for evaluating ALMs' EI. Using real-recorded human dialogues from the ICASSP 2026 HumDial Challenge, it reformulates emotional tracking and causal reasoning into multiple-choice questions with adversarial distractors, mitigating subjective scoring bias for cognitive tasks. It retains the generation of empathetic responses and introduces an acoustic-semantic conflict task to assess robustness against contradictory multimodal signals. Evaluations of eight ALMs reveal that most models struggle with multi-turn emotional tracking and implicit causal reasoning. Furthermore, all models exhibit decoupled textual and acoustic empathy, alongside a severe text-dominance bias during cross-modal conflicts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
