arXiv:2605.20712v1 Announce Type: new 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 d
The proliferation of context-dependent AI applications and the increasing linguistic diversity of AI users are driving the need for more nuanced ASR evaluation.
Improved diagnostic evaluation of ASR is crucial for advancing AI agent capabilities and deploying reliable AI across diverse linguistic and functional contexts, particularly in large, multilingual markets.
ASR development shifts from simple accuracy metrics like WER to more sophisticated, category-specific error analysis, enabling more targeted model improvements and better user experiences.
- · AI agents developers
- · Multilingual AI platforms
- · Indic language users
- · Specialized ASR applications
- · ASR models with high domain-specific error rates
- · Generic WER-focused ASR evaluation methodologies
More accurate and robust ASR systems for a wider range of languages and use cases.
Accelerated development and adoption of AI agents that rely on speech interaction.
Enhanced accessibility and utility of AI technologies for non-English speaking populations, fostering greater digital inclusion and economic participation.
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