SMART: Semantic Segment Mapping and Assessment of Recognition Transcripts with Contextual Rubrics
Word error rate (WER) assigns equal cost to lexical edits despite their different semantic consequences, while overall semantic scores cannot identify which individual errors alter the intended meaning. We formulate ASR semantic evaluation as context-aware, segment-level consequence assessment and propose Semantic Segment Mapping and Assessment of Recognition Transcripts with Contextual Rubrics (SMART), a framework for traceable semantic evaluation and targeted ASR correction. SMART maps reference-hypothesis discrepancies into coherent segments and applies a structured contextual rubric to assign explicit consequence labels. These labels support Semantic Impact Error Rate (SIER), which qu...
| Authors | Xiaoyu Gu, Chencheng Wang, Fuyang Li et al. |
|---|---|
| Primary category | cs.CL |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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