Word Error Rate
word-error-rate-532a6be3·2 events·first seen 28d agoAliases: Word Error Rate
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SN-WER: Script-Normalized Word Error Rate for Multi-Script Indic ASR Evaluation
Researchers propose Script-Normalized WER (SN-WER), a training-free evaluation metric that transliterates ASR reference and hypothesis text into a canonical script before computing WER, addressing overestimation of errors caused by script mismatches in multilingual settings. Evaluated across 5 Indic languages, 2 datasets, and 3 ASR models, SN-WER reduces inflated model performance gaps by up to 12% on curated FLEURS data and attenuates romanization-induced WER inflation by 67% in controlled tests. The metric maintains near-identical sensitivity to genuine semantic errors (ΔSN-WER/ΔWER ≈ 1.09) and shows robustness to transliterator choice with token-collision rates below 0.1%. The authors recommend SN-WER as a companion metric to WER and CER, particularly for pipelines feeding downstream search, indexing, or multilingual LLM applications.
Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers
This Hugging Face blog post provides a practical guide for fine-tuning OpenAI's Whisper model for multilingual automatic speech recognition using the Transformers library. It covers dataset preparation, training configuration, and evaluation using the Word Error Rate metric. The post targets practitioners seeking to adapt Whisper to low-resource or domain-specific languages.