想测语音AI聊天有多自然？SPEARBench从8个维度对比人类，告诉你现有模型的短板具体在哪。
SPEARBench基于Seamless Interaction语料库构建对话提示，评估多个当代语音到语音模型在响应延迟、中断、语音质量、ASR鲁棒性、语言与方言一致性、情感自然度、人际关系立场等8个维度上的表现。与人类对话相比，当前模型在延迟、重叠、方言保留、情感适应和立场动态方面仍有差距。基准包含原始人类回答作为参考条件。
SPEARBench: A Benchmark for Naturalness Evaluation in Streaming Speech-to-Speech Language Models
Streaming speech-to-speech language models aim to answer spoken queries directly with synthetic speech. However, standard speech and text benchmarks do not capture whether these systems behave naturally in conversations, where timing, turn-taking, prosody, interpersonal stance, language and dialect consistency, and relationship-aware appropriateness jointly shape perceived quality. We introduce SPEARBench, a benchmark for evaluating naturalness in speech-to-speech language models from question-answer interactions. SPEARBench constructs controlled dialogue prompts from the Seamless Interaction corpus, runs inference across multiple models, and evaluates generated answers using a multidimensional protocol that covers response latency, interruptions, speech quality, ASR robustness, language and dialect consistency, emotional naturalness, interpersonal stance, and explainable distributional baselines. The benchmark includes original human answers as a reference condition and reports results for several contemporary models. Results show that current models can achieve high signal-level quality and low ASR error while still differing from human conversational behavior in latency, overlap, dialect preservation, emotional adaptation, and interpersonal stance dynamics.