教学视频音频文本索引词的分析与可视化
Analysis and Visualization of Index Words from Audio Transcripts of Instructional Videos
摘要：
我们介绍了新的技术提取，分析和可视化的文本内容从教学视频的低生产质量。使用自动语音识别，从最初高度压缩的大学课程视频中获得近似的成绩单（H 75%单词出错率），每个包括10到30个讲座。然后，用伴随课程的书籍或论文形式的文本材料从看似不连贯的成绩单中过滤出有意义的短语。由此产生的索引到成绩单是绑在一起，并可视化在3个实验图，有助于理解整体课程结构，并提供了一个工具，本地化某些主题的索引。我们特别讨论了一个成绩单索引图，它以图形方式列出了一门课程的关键短语，一个教科书章节到成绩单匹配，最后是一个讲座成绩单相似性图，它聚类语义相似的讲座。我们在7个完整的课程中测试我们的方法和工具，有230小时的视频和273份成绩单。我们能够为给定的成绩单提取多达98个独特的关键词，为整个课程提取多达347个独特的关键词。教科书章节与成绩单匹配的准确率平均超过70%。所使用的方法可以应用于其中存在经常性主题词（新闻、体育、会议等）的视频类型。
英文摘要：
We introduce new techniques for extracting, analyzing, and visualizing textual contents from instructional videos of low production quality. Using Automatic Speech Recognition, approximate transcripts (H75% Word Error Rate) are obtained from the originally highly compressed videos of university courses, each comprising between 10 to 30 lectures. Text material in the form of books or papers that accompany the course are then used to filter meaningful phrases from the seemingly incoherent transcripts. The resulting index into the transcripts is tied together and visualized in 3 experimental graphs that help in understanding the overall course structure and provide a tool for localizing certain topics for indexing. We specifically discuss a Transcript Index Map, which graphically lays out key phrases for a course, a Textbook Chapter to Transcript Match, and finally a Lecture Transcript Similarity graph, which clusters semantically similar lectures. We test our methods and tools on 7 full courses with 230 hours of video and 273 transcripts. We are able to extract up to 98 unique key terms for a given transcript and up to 347 unique key terms for an entire course. The accuracy of the Textbook Chapter to Transcript Match exceeds 70% on average. The methods used can be applied to genres of video in which there are recurrent thematic words (news, sports, meetings,...)
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
