What Developers Need to Know About WER, KER, and KRR
Blog post from Deepgram
Speech-to-text technology has advanced substantially since early systems such as Bell Labs’ 1952 Audrey, but transcription errors remain important to measure because their consequences vary by context, from harmless conversational mistakes to potentially dangerous errors in medical documentation. The discussion distinguishes evaluation metrics from loss functions, noting that metrics assess model performance while loss functions are used to train models and must support optimization. Word Error Rate (WER), the most common speech-recognition metric, measures substitutions, insertions, and deletions across all words, making it useful for broad transcription comparisons but unable to reflect the differing importance or semantic impact of specific errors. Keyword Error Rate (KER) instead evaluates errors among predefined critical terms, making it suitable for applications such as compliance, call routing, voice commands, and healthcare, although it depends on selecting appropriate keywords in advance. Keyword Recognition Rate (KRR) is the complementary success-oriented measure of correctly recognized keywords and can be more useful for reporting, service agreements, and business dashboards, though it adds no technical information beyond KER. Choosing among these measures depends on whether complete transcript fidelity, critical-term detection, or both are required, with industries often combining WER and keyword-focused metrics to match their operational risks and goals.
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