AI + Education

Our convenient AI technology allows content creators to focus on quality video creation instead of the nitty-gritty manual setup.

AI使英语教学短视频制作效率提高10倍

我们的AI技术使内容创建者有更多时间来创作优质的短视频,并减少对人工的依赖。

Speech recognition

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.

Scene Recognition

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.

语音识别(STT)

吧啦吧啦基于业界领先的语音识别算法,结合教育行业及短视频的数据特性,自主研发了数据增广、语法矫正及文本对齐技术,实现了视频语音到文本的端到端学习。同时,吧啦吧啦的模型压缩技术极大增强视频语音处理的吞吐率和实时性,极大提升了用户视频编辑和录制效率。

场景识别(Scene Recognition)

吧啦吧啦拥有全套自主知识产权的场景识别及标记引擎,通过集成学习视频数据、语音数据及文本数据,吧啦吧啦实现了精准的视频场景及主题理解,为构建用户兴趣图谱、精细化挖掘和运营教育主题和风格赋能。

User Interest Profile

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 recommendation system.

Recommendation Sytem

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.

用户画像(User Profile)

在用户使用吧啦吧啦的过程中,吧啦吧啦用户画像系统对用户的理解也将更够透彻。吧啦吧啦打造了基于自然语言处理技术的用户特征识别及兴趣图谱挖掘引擎,挖掘得到的用户社会属性及兴趣属性为吧啦吧啦精准运营及智能推荐提供了夯实的技术基础。

推荐系统(Recommendation System)

吧啦吧啦自研打造了基于深度强化学习的推荐系统。相较于业界常规推荐系统,吧啦吧啦的推荐引擎结合了音视频及文本等多维数据、基于兴趣图谱及社会属性的用户画像,以及基于用户操作流的实时兴趣分析,使能吧啦吧啦的推荐引擎信息密度更高,实时兴趣匹配度更精准,内容推荐效果更好。