Written transcripts of the same speech can differ in ways that are not uniquely determined by the speech signal.
We formulate evaluation-side transcript canonicalization (ETC), which maps multiple surface forms to a single canonical form when their differences are not determined by speech.
To our knowledge, ETC-Bench is the first and most comprehensive multilingual benchmark for evaluation-side transcript canonicalization, covering 181 phenomenon types across 12 languages.
Each instance is a minimal pair that shares a sentence context and differs only in a target span exhibiting one phenomenon.
To evaluate both canonicalization success and semantic preservation, we propose Semantic-preserving Exact-match Normalization Accuracy (SENA), which combines exact agreement between independently canonicalized pair members with a pair-relative BERTScore constraint against information loss.
We further introduce the Taxonomy-guided Agent Canonicalization Skill (TACS), which provides LLM agents with phenomenon descriptions and explicit canonicalization contracts.
Experiments with five baseline configurations and three TACS-based agents show that the agents outperform the baseline systems on shared languages.
ETC-Bench provides a systematic testbed for evaluation-side transcript canonicalization across languages.