Improving Tool-Call Robustness in Companion Robot Agents with ASR N-Best and Clarification
Year: 2026
Type: conference-paper
Abstract: For companion and human-care service robots, a single ASR transcription error can silently redirect a spoken request to the wrong action, eroding user trust that makes social human-robot interaction meaningful. We evaluate whether combining two existing robustness mechanisms, structured N-best ASR uncertainty exposure and constrained one-turn clarification, improves final tool selection before tool execution. Using a 2 × 2 factorial design plus an oracle upper bound, we evaluate five conditions across 22 tool categories on a 440-utterance English benchmark. Across five repeated runs, ASR degradation alone reduces tool-call accuracy by an average of 3.00 percentage points relative to oracle text. The combined Nbest + clarification setting reaches a mean of 93.23% final tool success against 91.77% for the ASR 1-best baseline, a mean gain of 1.46 percentage points with positive improvement in all five repetitions. These results show that the combined N-best + clarification setting is an effective and reliable strategy for improving tool-selection reliability of speech-driven companion and home care robots.
Source: IEEE RO-MAN
Authors: Jonghun Shin, Kooksung Jun, Deok-Won Lee
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7220919486