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We propose an alternative evaluation metric to Word Error Rate (WER) for the decision audit task of meeting recordings, which exemplifies how to evaluate speech recognition within a legitimate application context. Using machine learning on an initial seed of human-subject experimental data, our alternative metric handily outperforms WER, which correlates very poorly with human subjectsf success in finding decisions given ASR transcripts with a range of WERs.