Most AI transcription tools use English-based models that struggle with the nuances of Japanese. At Clipa, we've taken a Japanese-first approach to subtitle accuracy. Here's the technical background.
Why Japanese subtitles are hard
- Homophones — Words like 橋/箸 (hashi: bridge/chopsticks) require contextual understanding to get right.
- Spoken vs. written gap — Casual speech patterns need judgment to convert into readable text.
- Technical terms and proper nouns — Gaming, business, and personal names are common failure points for general models.
- Line breaks — Readable subtitle breaks depend on screen size and reading speed, not just grammar.
Our approach
① Japanese-focused language model
Rather than a general multilingual model, we use one fine-tuned intensively on Japanese audio data across YouTube, podcasts, lectures, and conversations.
② Extended context window
We process audio with a wider context window, so homophone resolution considers the topic of the full video — not just the surrounding few words.
③ Post-processing pipeline
After transcription, we apply automatic punctuation, filler removal, and optimized line-break placement.
④ Continuous improvement
When users correct subtitles, anonymized corrections feed back into training. The model improves daily, especially for niche vocabulary.
What's ahead
We're currently working on speaker identification and genre-specific dictionaries for gaming, beauty, and business content.