EchoMind: 一种相互关联的多级基准，用于评估共情语音语言模型
EchoMind: An Interrelated Multi-level Benchmark for Evaluating Empathetic Speech Language Models
AI总结：
EchoMind是一种多级基准，用于评估SLMs在共情对话中的能力，揭示了现有模型在处理高表现性语音线索方面的不足。
摘要：
语音语言模型（SLMs）在口语语言理解方面取得了显著进展。然而，尚不清楚它们是否能够完全感知非词语音线索并回应具有情感和上下文因素的共情。现有基准通常单独评估语言、声学、推理或对话能力，忽视了这些技能整合对人类样式的、情感智能对话的重要性。我们提出了EchoMind，这是首个相互关联、多级基准，通过顺序、上下文关联的任务模拟共情对话的认知过程：口语内容理解、语音线索感知、整合推理和回应生成。所有任务共享相同且语义中性的脚本，不含显式情感或上下文线索，并通过语音风格的受控变化来测试交付效果，而不受转录影响。EchoMind基于一个以共情为导向的框架，涵盖3个粗粒度和12个细粒度维度，包含39个语音属性，并通过客观和主观指标进行评估。测试12种先进的SLMs发现，即使是最先进的模型在高表现性语音线索上也存在困难，限制了共情回应质量。对提示强度、语音来源和理想语音线索识别的分析揭示了持续存在的指令遵循、对自然语音变化的不耐受以及有效使用语音线索进行共情的弱点。这些结果突显了需要将语言内容与多样语音线索整合的SLMs，以实现真正共情的对话能力。
英文摘要：
Speech Language Models (SLMs) have made significant progress in spoken language understanding. Yet it remains unclear whether they can fully perceive non lexical vocal cues alongside spoken words, and respond with empathy that aligns with both emotional and contextual factors. Existing benchmarks typically evaluate linguistic, acoustic, reasoning, or dialogue abilities in isolation, overlooking the integration of these skills that is crucial for human-like, emotionally intelligent conversation. We present EchoMind, the first interrelated, multi-level benchmark that simulates the cognitive process of empathetic dialogue through sequential, context-linked tasks: spoken-content understanding, vocal-cue perception, integrated reasoning, and response generation. All tasks share identical and semantically neutral scripts that are free of explicit emotional or contextual cues, and controlled variations in vocal style are used to test the effect of delivery independent of the transcript. EchoMind is grounded in an empathy-oriented framework spanning 3 coarse and 12 fine-grained dimensions, encompassing 39 vocal attributes, and evaluated using both objective and subjective metrics. Testing 12 advanced SLMs reveals that even state-of-the-art models struggle with high-expressive vocal cues, limiting empathetic response quality. Analyses of prompt strength, speech source, and ideal vocal cue recognition reveal persistent weaknesses in instruction-following, resilience to natural speech variability, and effective use of vocal cues for empathy. These results underscore the need for SLMs that integrate linguistic content with diverse vocal cues to achieve truly empathetic conversational ability.
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。
