🎙 ASR Showdown: Clova Note vs. Daglo in Korean— Which One Really Listens?
- The Dr.K
- 17 minutes ago
- 2 min read
Capturing the real voice of the user is only half the battle. The other half is getting an automatic-speech-recognition (ASR) system that won’t leave you cleaning up a transcription disaster just when the analysis clock starts ticking. We put two of Korea’s most popular ASR tools head-to-head so you don’t have to.
🧪 Research Setup
Item | Details |
Session type | In-person focus-group discussion (FGD) |
Participants | 8 FPS PC-game players + 1 moderator |
Recording length | ≈ 2 hours |
Source audio | Single track fed unchanged into both ASR tools |
🔍 Evaluation Criteria
Context integrity – Does the conversation flow naturally?
Sentence completeness – Are utterances grouped into full sentences?
Noise & typos – How many split fragments or obvious errors?
Analysis readiness – How easy is speaker or topic tagging?
📊 Numbers at a Glance
Metric | Daglo | Clova Note |
Total lines | 1 ,050 | 623 |
Ultra-short lines (≤ 5 characters) | 381 | 0 |
Suspected incomplete sentences | 552 | 173 |
Text overlap with Clova (baseline) | – | 68.1 % |
Why it matters: Each extra fragment means one more manual merge or deletion before you can even start coding the data.
🧠 What the Numbers Mean
Daglo nails micro-level timestamps, but its aggressive splitting chops a continuous discussion into hundreds of tiny fragments. Analysts lose context and burn time re-stitching sentences.
Clova Note keeps utterances bundled into coherent blocks. For long FGDs and 1-on-1 interviews, that translates into hours saved on clean-up and faster passage to thematic coding.
✅ Bottom Line
Clova Note is the safer bet when context and post-processing time matter.
Accuracy is only the first filter. In real-world UX research, a transcription tool must also preserve narrative structure and minimize analyst effort. On those two fronts, Clova pulls ahead.
🧪 Next Up on UXR Player
“Can Korean ASR pick up emotional nuance?”Stay tuned as we stress-test sentiment layers across multiple speech engines—and share every win, fail, and workaround.