Our convenient AI technology allows content creators to focus on quality video creation instead of the nitty-gritty manual setup.
Combined state of the art speech recognition and unique data characteristics of the
education industry and short video, we utilize data augmentation, grammar correction and text alignment
technology to develop an end-to-end video speech to text algorithm.
The company has developed our IPs for scene recognition and tagging engine. By
integrating UGC (user-generated content, eg. video, voice and text data), we have improved a 10 percent
improvement of understanding video scenes. This helps us to build user interest maps, and automatically
assign tags and categorize videos into educational themes so we can recommend videos based on user’s
English language level and user interests.
Through user interest graph, we use natural language processing to build a mining
engine to match a user with topics they might like. This provides a solid foundation for our
Compared with the industry's conventional recommendation system, the
recommendation engine combines multi-dimensional data such as audio and video and text, user interest
maps, and real-time interest analysis based on user flow. The recommendation engine achieved more
accurate real-time interest matching and better content recommendation.