How to flag low-confidence spans in AI meeting transcripts for reviewer QA
Blog post from Gladia
AI meeting transcription systems face challenges in accurately capturing and verifying audio content due to transcription errors that appear grammatically correct but can silently corrupt data entries. To address this, Gladia's asynchronous API offers word-level confidence scores, which highlight low-confidence spans for targeted human review, thereby enhancing the reliability of AI-generated transcripts. The goal is to provide verifiable, not perfect, transcripts by focusing on areas where the model is uncertain, using word-level scores to pinpoint potential errors more effectively than segment-level scores. Reviewers are guided to verify only flagged sections, which reduces the burden of full transcript reviews and maintains productivity in remote and distributed teams. Factors like background noise, distant microphones, specialized vocabulary, and accented speech can lower transcription confidence, and Gladia's system allows for dynamic calibration of confidence thresholds to improve accuracy. The system emphasizes the importance of precise QA workflows that direct reviewers to specific spans needing attention, enhancing trust and efficiency in AI-driven transcription processes.
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