# SN-WER: Script-Normalized WER for Multi-Script Indic ASR Evaluation

arXiv: https://arxiv.org/abs/2606.02548 · HTML: https://mlsanity.com/paper/2606.02548/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 1 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [Advocating Character Error Rate for Multilingual ASR Evaluation](https://mlsanity.com/paper/2410.07400/) (2024)
- [What is lost in Normalization? Exploring Pitfalls in Multilingual ASR Model Evaluations](https://mlsanity.com/paper/2409.02449/) (2024)
- [Lenient Evaluation of Japanese Speech Recognition: Modeling Naturally Occurring Spelling Inconsistency](https://mlsanity.com/paper/2306.04530/) (2023)
- [WER We Stand: Benchmarking Urdu ASR Models](https://mlsanity.com/paper/2409.11252/) (2024)
- [WERd: Using Social Text Spelling Variants for Evaluating Dialectal Speech Recognition](https://mlsanity.com/paper/1709.07484/) (2017)

## Abstract (arXiv)

Word Error Rate (WER) is the dominant metric for automatic speech recognition (ASR), but it can overestimate errors when references and hypotheses encode the same words in different scripts. This issue is common in multilingual settings where ASR models may emit romanized text. We propose Script-Normalized WER (SN-WER), a training-free, evaluation-only scoring method that transliterates both reference and hypothesis text into a language-specific canonical script before computing WER. We evaluate SN-WER on 5 Indic languages, 2 datasets, and 3 ASR models. On curated FLEURS data, SN-WER reduces inflated model gaps by up to 12%, while on noisier Common Voice data the reductions are smaller or inconsistent, indicating genuine recognition weaknesses rather than only script mismatch. Controlled stress tests show a 67% attenuation of artificial romanization-induced WER inflation, while lexical-substitution controls show near-identical sensitivity to semantic errors, with Delta SN-WER / Delta WER approximately 1.09. SN-WER is robust to transliterator choice, normalization changes, and shows low token-collision rates below 0.1% in the evaluated Indic setting. We argue that SN-WER should be reported alongside WER and CER as a companion metric for script-insensitive ASR evaluation, especially when transcripts feed downstream search, indexing, or multilingual LLM pipelines.