At Kuky, we’re building a social network focused on peer support for mental and health-related challenges. One of our key features is real-time video transcription combined with sentiment analysis, aimed at enhancing user experience and facilitating meaningful conversations.
For real-time transcription, we leverage WebRTC for video streaming and integrate speech-to-text model from OpenAI. The transcribed text is then processed using NLP models to analyze sentiment and highlight emotional cues in real time conversation.
Some of the biggest hurdles we faced:
- Latency: Achieving real-time transcription with minimal delay.
- Accuracy: Balancing speed and accuracy, especially with diverse accents and noisy environments.
- Privacy & Security: Handling sensitive conversations while maintaining user trust and data security.
- UI/UX Considerations: Displaying sentiment analysis feedback in a way that’s useful but not intrusive.
These are all things we know we can improve so we’re looking for feedback
I’d love to hear from others who’ve worked on real-time NLP applications. How have you tackled issues like low-latency transcription or contextual sentiment analysis? Are there better approaches for handling sensitive user data in these scenarios?
Happy to discuss and share more details!
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