@article{chen-etal-2026-voicebench,
    title = "{V}oice{B}ench: Benchmarking {LLM}-Based Voice Assistants",
    author = "Chen, Yiming  and
      Yue, Xianghu  and
      Zhang, Chen  and
      Gao, Xiaoxue  and
      Tan, Robby T.  and
      Li, Haizhou",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "14",
    year = "2026",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/ingest-latest-mitpress-cl-tacl/2026.tacl-1.18/",
    doi = "10.1162/tacl.a.628",
    pages = "378--398",
    abstract = "Recent advancements in large language models (LLMs) like GPT-4o have enabled real-time speech interactions through LLM-based voice assistants, offering an improved user experience over text-based interactions. However, a suitable benchmark to rigorously evaluate such speech interactions systems is currently lacking. To bridge this gap, we introduce VoiceBench, the first benchmark specifically designed to assess LLM-based voice assistants. VoiceBench comprises 6,783 synthetic and real spoken instructions recorded from diverse speakers across eight distinct tasks. These instructions are meticulously crafted to assess three crucial capability areas: general knowledge, instruction-following, and safety compliance. Furthermore, VoiceBench systematically incorporates realistic variations common in spoken interactions, including differences in speaker characteristics (e.g., accents), heterogeneous environmental conditions (e.g., reverberation), and content complexities such as mispronunciations. Extensive experiments reveal the limitations of current LLM-based voice assistant models and offer valuable insights for future research and development in this field.1"
}