My data, my benchmark: Swapping the speech recognition (ASR) engine for my lecture note system
Introduction: Why official scores aren’t enough My workflow for turning recorded lectures into notes goes in a straight line: transcript → matching slides → final summary. The foundation of this line is automatic speech recognition (ASR). However much it mishears, every subsequent layer of notes will be just as wrong. Originally, I was using OpenAI’s Whisper large-v3. The starting point for all this was actually me scrolling on my phone and seeing MediaTek Research announce their Taiwanese model, Breeze-ASR-26. A Taiwanese model is intriguing enough on its own, but clicking through, I learned they had already released Breeze-ASR-25 last year. Fine-tuned from Whisper-large-v2, it was optimized specifically for Taiwanese Mandarin and Mandarin-English code-switching, and open-sourced under Apache 2.0. Medical lectures happen to be ground zero for Mandarin-English code-switching. I had somehow missed this for a whole year. ...