Why Word Error Rate Is Broken for Indian Languages: The BRIDGE 7-Metric Stack Explained

The rapid expansion of India's voice AI market, covering 22 scheduled languages and numerous dialects, highlights the inadequacy of the Word Error Rate (WER) metric, which was originally developed for English, in accurately assessing the performance of speech recognition systems for Indian languages. WER fails due to differences in word boundaries, morphological agglutination, script diversity, and code-switching, causing inflated error scores. To address these challenges, the BRIDGE 7-metric framework is proposed as a more comprehensive evaluation tool. It incorporates metrics such as BERTScore for semantic similarity, Entity F1 for entity recognition, and Character Error Rate (CER) for grapheme-level errors, among others, to provide a fuller picture of transcription quality. The framework emphasizes the need for multi-metric evaluation in speech-to-text pipelines, using tools like jiwer and HuggingFace evaluate, and highlights the importance of text normalization in reducing inflated error rates. The BRIDGE approach aims to better align evaluation with user outcomes, moving away from English-centric assumptions, and is crucial for developing voice AI systems that are effective across the diverse linguistic landscape of India.

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