Transcription Quality Monitoring: How to Know Your STT Still Works

Transcription quality monitoring is crucial for ensuring the accuracy of speech-to-text (STT) systems in real-world conditions, as benchmarks often fail to capture the dynamic nature of production environments. The article outlines methods for detecting transcription accuracy issues through drift detection, focusing on four types of drift: acoustic, codec, vocabulary, and population. It emphasizes the need for comprehensive metrics beyond Word Error Rate (WER), such as Character Error Rate (CER), Keyphrase Error Rate (KER), confidence scores, and latency percentiles, to understand underlying problems and protect user experience. Sampling strategies are proposed to make labeling manageable, while alert architectures are recommended for timely responses to detected issues. The piece concludes with a four-week implementation plan to establish a robust monitoring system, suggesting that only through active transcription quality monitoring can one maintain reliability in STT systems as conditions change.