Audio2Tool: Speak, Call, Act -- A Dataset for Benchmarking Speech Tool Use
Voice assistants increasingly rely on Speech Language Models (SpeechLMs) to interpret spoken queries and execute complex tasks, yet existing benchmarks lack domain breadth, acoustic diversity, and compositional reasoning complexity to evaluate tool-calling performance. We introduce Audio2Tool, a large-scale dataset comprising approximately 30,000 queries designed to assess tool-calling capabilities of SpeechLMs across three primary domains: Smart Car, Smart Home, and Wearables. Our benchmark features a multi-tier complexity hierarchy, ranging from simple direct commands to complex multi-intent and needle-in-a-haystack extraction to isolate distinct failure modes. To ensure realism, we emp...
| Authors | Ramit Pahwa, Apoorva Beedu, Parivesh Priye et al. |
|---|---|
| Primary category | cs.SD |
| Published | 17 April 2026 |
| Last updated at source | 28 April 2026 |
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