SCRIBE: Diagnostic Evaluation and Rich Transcription Models for Indic ASR
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Computer Science > Computation and Language
Title:SCRIBE: Diagnostic Evaluation and Rich Transcription Models for Indic ASR
Abstract:Automatic speech recognition replaces typing only when correction costs less than manual entry, a threshold determined by error types, not counts: fixing a misrecognized domain term costs far more than inserting a comma. Word error rate (WER) fails on two fronts: it collapses distinct error categories into a single scalar, and it structurally penalizes agglutinative languages where valid sandhi merges inflate scores. We introduce SCRIBE, a diagnostic framework that provides categorical error decomposition into lexical, punctuation, numeral, and domain-entity rates through sandhi-tolerant alignment with domain vocabulary injection. Human validation confirms SCRIBE aligns with expert judgment where WER does not. We release SCRIBE, an LLM curation pipeline, benchmarks, and open-weight rich transcription models for Hindi, Malayalam, and Kannada.
| Comments: | Submitted to Interspeech 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.20712 [cs.CL] |
| (or arXiv:2605.20712v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.20712
arXiv-issued DOI via DataCite (pending registration)
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