The 73-Second Hallucination: Building Automated QA for Generative TTS
In my previous article, I wrote about solving ear fatigue in long-form TTS: repeated cadence, unnatural pacing, and the tendency for generated narration to fall into predictable patterns. One of the conclusions I came away with was that higher temperature gave the model more room to vary its performance without changing the words. Then I listened to the final master of Chapter 9. At 03:48 , the…
The groundbreaking article discusses the challenges of ensuring accuracy in automated text-to-speech (TTS) systems. The author explains that the model tends to create unnatural cadence and predictable patterns, especially during lengthy narration. To address this, they developed a Multimodal Script Verification Pipeline to automatically compare generated audio against the original script.
The pipeline required treating the script as the ground truth and flagging any discrepancies such as hallucinations, omissions, substitutions, or repeated passages. While most of the 13 chapters were clean, Chapter 9 presented a major issue where the model generated an unplanned 73-second argument, highlighting the limitations of automated QA.
The author emphasizes that performance review and fidelity verification are distinct tasks and that the final step involves human review of flagged discrepancies and repairs before releasing the audiobook.
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