SBIR Phase II: A Cloud-Based Service for Audio Access to News and Blogs

这个SBIR第二阶段项目将研究基于现有书面内容（如新闻和博客）自动发现用户感兴趣主题的算法。然后可以使用这些主题动态地创建具有高质量合成音频的个性化内容阅读器。从这个基于云的项目中创建的应用程序将使汽车司机能够在不离开道路的情况下获得即时和相关的信息，这是当今漫长通勤和车内安全所必需的。其他应用程序将允许那些视力受损或忙于锻炼、园艺等的人通过智能手机收听互联网新闻和博客。这个项目对盲人、老化的眼睛以及汽车司机都有社会影响，因为他们在阅读小字体和小屏幕上有困难，因为它提供了一种“不需要眼睛”的方式来查找上下文新闻和博客，并提供了一种轻松的聆听体验。本项目的基础研究内容可以重新应用于其他内容和类似领域的研究。该项目的应用有可能产生收入流，从而创造就业机会，并对经济产生整体影响。该研究的目标是确定使用无监督机器学习从大量不相关文档的语料库中提取的主题信息是否可用于内容发现，提高合成语音的质量，并发现推荐系统的用户偏好。该研究使用主题建模（一种机器学习算法）来发现数千个RSS提要中的主题，并使用自然语言处理来提高合成语音的质量。然后可以使用主题和RSS通道之间的映射概率，根据用户的主题首选项检索特定的RSS通道。由于通过听力来扫描内容比通过视觉来扫描相关响应的过程要慢，因此目前的研究计划将通过将用户偏好与信息检索相结合来改进这一过程，以获得更好的用户体验。主题发现研究将包括三个关键组成部分：发现多级子主题以创建更容易浏览的主题层次结构，识别趋势主题的方法，以及确定当前和相关的主题以实现音频内容的自动化。一旦证明该项目产生了有效的结果，就可以将相同的技术应用于其他文档集合。

This SBIR Phase II project will research the algorithms for automatic discovery of topics of interest for a user based on existing written content such as news and blogs. These topics can then be used to dynamically create a personalized content reader with high quality synthesized audio. Applications created from this cloud-based project will enable a car driver to get instant and relevant information without taking her eyes off the road, a must for today's lengthy commutes and in-car safety. Other applications will allow the same audio access to Internet news and blogs via a smartphone for those who are vision-impaired or vision-busy with exercising, gardening, etc. This project has a societal impact among the blind, aging eyes that have difficulty reading small print and small screens, and the car driver, since it provides the ability to find contextual news and blogs in an "eyes-free" manner and with an easy listening experience. The fundamental research components from this project can be re-applied to other content and similar fields of research. This project's applications have the potential to generate a revenue stream which will in turn create jobs and have an overall impact on the economy.The goal of the research is to determine whether topic information extracted from a large corpus of unrelated documents using unsupervised machine-learning can be used for content discovery, improving the quality of synthesized speech, and discovering user preferences for a recommendation system. The research uses topic-modeling, a machine learning algorithm, to uncover topics across thousands of RSS feeds, and natural language processing to improve the quality of synthesized speech. Retrieval of specific RSS channels per user's topic preferences is then possible by using the probability of mappings between topics and RSS channels. Since content scanning by listening is a slower process than visually scanning for relevant responses, the current research proposal will improve this process by combining user preference with information retrieval for a better user experience. The topic discovery research will include three key components: discovering multiple levels of subtopics to create topic hierarchies for easier browsing, a method for identifying trending topics, and determining current and relevant topics for automation of audio content. Once this project is shown to produce effective results, the same techniques can be applied across other document collections.