This solution brief explains how to properly evaluate modern deep learning ASR platforms versus legacy trigram-based systems. It outlines the evolution from 1st and 2nd generation speech recognition models to fully end-to-end deep neural networks powered by GPUs. The guide emphasizes defining evaluation criteria such as word error rate (WER), usability thresholds, scale, speed, and deployment flexibility. It warns against overreliance on keyword boosting and highlights the importance of training models on representative audio. Enterprises are encouraged to test with real-world data, assess latency, and align ASR performance with long-term business goals.

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