Code-switching best practices: testing and quality assurance

Code-switching, the practice of alternating between languages within a conversation, poses significant challenges for monolingual word error rate (WER) testing in automatic speech recognition (ASR) systems, as it conceals up to 50% degradation in performance. This text highlights the importance of using Point-of-Interest Error Rate (PIER) over WER to specifically target errors at language switch points, thus revealing hidden regressions that degrade customer relationship management (CRM) entries, coaching scores, and meeting summaries. It emphasizes the need for a robust quality assurance (QA) pipeline that incorporates diverse and representative datasets like CS-FLEURS, LinCE, and SLR104, and establishes per-language thresholds to block faulty builds in continuous integration and continuous delivery (CI/CD) processes before they reach production. By adopting canary deployments and documented rollback policies, organizations can preemptively address code-switching failures, ensuring accurate multilingual ASR performance in real-world conditions. The text also underscores the significance of shift-left testing to catch regressions early, reducing the cost and impact of errors in production.

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