Automatic indexing for lecture speech and its advanced utilization through speech interaction

我们收集了16位演讲者、114篇演讲、3860分钟的课堂演讲，并对语料进行了分析。我们开发了自动语音识别，句子提取，分割/索引，口语检索和讲座浏览系统的建设，我们大学的研究生课程的课堂教学数据的过程。这些过程是必要的，以提高广播声音或视频数据的可用性在讲座的情况下，摘要和索引讲座演讲或视频使学生能够更有效地学习。我们的目标是构建一个这样的结构化讲座内容的框架。为了实现这一目标，首先，我们研究了录音方法对语音识别性能的影响。事实证明，高质量的手持麦克风和低质量的翻领麦克风之间的准确性相差23%。此外，我们改进了领域相关的语言模型，使用相关的Web文本，并开发了一个填充插入模型。其次，我们尝试通过提取重要句子来自动摘要，我们获得了0.319-0.456的κ值，与人类的0.407-0.477相当。最后，我们利用上述过程的结果，建构了一个能让使用者更有效学习的讲座浏览系统，并对其进行了评估

We collected the class room lecture speech consisting of 16 speakers, 114 lectures, and 3860 minutes, and publised the corpus. We developed the procedure of automatic speech recognition, sentence extraction, segmentation/indexing, spoken retrieval and construction of lecture browsing system for classroom lecture data of our university's graduated course. These processes axe necessary to improve the usability of broadcasting sound or video data In the case of lecture, summarized and indexed lecture speech or video enables to students to more effective leaning. Our goal was to construct a framework of such structured lecture contents. To achieve this goal, first, we investigated influence of the recording methods on the speech recognition performance. It turned out that there was 23% difference on the accuracy between a high quality hand-microphone and a low quality lapel microphone. Furthermore, we improved the domain-dependent language model by using related Web texts and developed a filler insertion model. Second, we tried automatic summarization by extracting important sentences, and we obtained 0.319-0.456 κ value, comparable with human doing 0.407-0.477. Finally, we constructed the lecture browsing system which enables users to learn more effectively by using results of the procedure described above, and evaluated it