Accurate transcription requires training the model on personal proper nouns and context
You need to train the model on proper nouns. When I mention Matt and Joel, I’m talking about my business partners. When I mentioned Matthew, that’s the guy from Ireland. I should be able to track every time I mentioned them forever, locally.
Standard transcription models are trained on generic datasets. They know common names and places, but they don’t know your specific vocabulary:
- Business partner names
- Project codenames
- Industry-specific terminology
- Personal abbreviations and shortcuts
- Location names that matter to you
The solution requires Local systems require Enhancement Dashboards for user-in-the-loop model training. You need an interface to teach the system your specific context.
Training approach:
- Initial raw transcription: Capture everything, even if imperfect
- Correction interface: Mad Libs-style editing to fix errors
- Entity recognition: Tag people, places, projects as you correct
- Context building: Link entities to relationships and background information
- Continuous improvement: System learns from corrections over time
This enables Deeply personalized AI can analyze relationships and optimize communication strategies. When the system accurately captures who you’re talking about, it can provide meaningful relationship insights.
The payoff is massive: instead of spending time cleaning up transcripts, the system gets better at understanding your specific communication patterns and vocabulary.