VoiceAssistant-Eval: Benchmarking AI Assistants across Listening, Speaking, and Viewing

• URL: http://arxiv.org/abs/2509.22651v1
• Date: Fri, 26 Sep 2025 17:59:59 GMT
• Title: VoiceAssistant-Eval: Benchmarking AI Assistants across Listening, Speaking, and Viewing
• Authors: Ke Wang, Houxing Ren, Zimu Lu, Mingjie Zhan, Hongsheng Li,
• Abstract summary: VoiceAssistant-Eval comprises 10,497 curated examples spanning 13 task categories.<n>To demonstrate its utility, we evaluate 21 open-source models and GPT-4o-Audio.<n>Results reveal three key findings: proprietary models do not universally outperform open-source models.
• Score: 45.15289852736435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The growing capabilities of large language models and multimodal systems have spurred interest in voice-first AI assistants, yet existing benchmarks are inadequate for evaluating the full range of these systems' capabilities. We introduce VoiceAssistant-Eval, a comprehensive benchmark designed to assess AI assistants across listening, speaking, and viewing. VoiceAssistant-Eval comprises 10,497 curated examples spanning 13 task categories. These tasks include natural sounds, music, and spoken dialogue for listening; multi-turn dialogue, role-play imitation, and various scenarios for speaking; and highly heterogeneous images for viewing. To demonstrate its utility, we evaluate 21 open-source models and GPT-4o-Audio, measuring the quality of the response content and speech, as well as their consistency. The results reveal three key findings: (1) proprietary models do not universally outperform open-source models; (2) most models excel at speaking tasks but lag in audio understanding; and (3) well-designed smaller models can rival much larger ones. Notably, the mid-sized Step-Audio-2-mini (7B) achieves more than double the listening accuracy of LLaMA-Omni2-32B-Bilingual. However, challenges remain: multimodal (audio plus visual) input and role-play voice imitation tasks are difficult for current models, and significant gaps persist in robustness and safety alignment. VoiceAssistant-Eval identifies these gaps and establishes a rigorous framework for evaluating and guiding the development of next-generation AI assistants. Code and data will be released at https://mathllm.github.io/VoiceAssistantEval/ .

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