Handling transcription hallucinations in meeting notes: detection and mitigation strategies

Transcription hallucinations in meeting notes are a significant issue, as they introduce fabricated, yet plausible text, which can corrupt downstream systems reliant on these notes. Addressing this requires a robust QA pipeline incorporating multiple layers: a speech-to-text (STT) model like Gladia's Solaria-1, which handles real-world audio conditions and provides word-level confidence scores, a confidence thresholding system to flag uncertain text, and a Large Language Model (LLM) validation layer to catch semantic inconsistencies. Confidence scores alone are insufficient since models often assign high confidence to hallucinated outputs, necessitating LLM validation for overconfident errors. Common triggers for hallucinations include silence gaps, crosstalk, and low-volume audio, with code-switching being a particularly challenging trigger for monolingual-trained models. Gladia's Solaria-1 addresses these issues natively by reducing hallucination triggers and providing structured data for error detection. Additionally, human-in-the-loop feedback mechanisms and continuous monitoring of confidence metrics are crucial for maintaining accuracy and preventing model drift over time.

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