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SCRIBE: Diagnostic Evaluation and Rich Transcription Models for Indic ASR

2026-05-20 05:09:01
Kavya Manohar, Arghya Bhattacharya, Kush Juvekar, Kumarmanas Nethil
arXiv_CL
         
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.

Abstract (translated)

URL

https://arxiv.org/abs/2605.20712

PDF

https://arxiv.org/pdf/2605.20712.pdf

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