When Does an ASR Error Matter? Task-Role Diagnostics for Tool-Using Voice Agents

Title:When Does an ASR Error Matter? Task-Role Diagnostics for Tool-Using Voice Agents

Abstract:In cascaded voice agents, ASR errors can have markedly different downstream consequences: a small transcription error may leave a tool call unchanged or alter its intent or arguments. Conventional word error rate (WER), however, does not capture these differences. We introduce SpeechEvalX, a paired diagnostic protocol that evaluates how task roles affected by ASR errors influence structured tool calls. Controlled edits are matched by source utterance, edit count, and operation, while task-role labels are compared with lexical severity, BERT cosine distance, Semantic-WER, and binary criticality. Across 594 source utterances, joint call accuracy falls from 82.3% with reference transcripts to 71.9-74.9% across three ASR front ends. Number and negation edits cause 21.9-25.7 percentage-point losses, versus 1.4-2.9 points for control edits. Detailed task-role labels improve source-grouped ROC-AUC from 0.73 to 0.78 over binary criticality, showing that downstream impact depends strongly on the task role affected.

Computation and Language (cs.CL)

arXiv:2610.17619 [cs.CL]

(or arXiv:2610.17619v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.17619 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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