It takes very long time to go through a complete online course. Without proper background, it is also difficult to understand retrieved spoken paragraphs. This paper therefore presents a new approach of spoken knowledge organization for course lectures for efficient personalized learning. Automatically extracted key terms are taken as the fundamental elements of the semantics of the course. Key term graph constructed by connecting related key terms forms the backbone of the global semantic structure. Audio/video signals are divided into multi-layer temporal structure including paragraphs, sections and chapters, each of which includes a summary as the local semantic structure. The interconnection between semantic structure and temporal structure together with spoken term detection jointly offer to the learners efficient ways to navigate across the course knowledge with personalized learning paths considering their personal interests, available time and background knowledge. A preliminary prototype system has also been successfully developed.

Spoken documents are audio signals and are thus not easily displayed on-screen and not easily scanned and browsed by the user. It is therefore highly desirable to automatically construct summaries, titles, latent topic trees and key term-based topic labels for these spoken documents to aid the user in browsing. We refer to this as semantic analysis and organization. Also, as network content is both copious and dynamic, with topics and domains changing everyday, the approaches here must be primarily unsupervised. We propose a framework for unsupervised semantic analysis and organization of spoken documents and for this purpose propose two measures derived from latent topic analysis: latent topic significance and latent topic entropy. We show that these can be integrated into an application system, with which the user can more easily navigate archives of spoken documents. Probabilistic latent semantic analysis is used as a typical example approach for unsupervised topic analysis in most experiments, although latent Dirichlet allocation is also used in some experiments to show that the proposed measures are equally applicable for different analysis approaches. All of the experiments were performed on Mandarin Chinese broadcast news.

This paper proposes a set of approaches to automatically extract key terms from spoken course lectures including audio signals, ASR transcriptions and slides. We divide the key terms into two types: key phrases and keywords and develop different approaches to extract them in order. We extract key phrases using right/left branching entropy and extract keywords by learning from three sets of features: prosodic features, lexical features and semantic features from Probabilistic Latent Semantic Analysis (PLSA). The learning approaches include an unsupervised method (K-means exemplar) and two supervised ones (AdaBoost and neural network). Very encouraging preliminary results were obtained with a corpus of course lectures, and it is found that all approaches and all sets of features proposed here are useful.

This paper presents a new approach of organizing the course lectures (as spoken documents) for efficient learning on demand by the users. By the properly matching the course lectures with the slides used, we divide the course lectures into hierarchical ldquomajor segmentsrdquo with variable length based on the topics discussed. Key term extraction, hierarchical summarization and semantic structuring are then performed over these ldquomajor segmentsrdquo. A key term graph is also constructed, based on which the various major segments of the course can be linked. In this way, the user can ask questions to the system, and develop his own road map of learning the knowledge he needs considering his available time and his background knowledge, based on the semantic structure provided by the system. A preliminary prototype system has been successfully developed with encouraging initial test results.

Automatic title generation for spoken documents is believed to be an important key for browsing and navigation over huge quantities of multimedia content. A new framework of automatic title generation for Chinese spoken documents is proposed in this paper using a delicate scored Viterbi algorithm performed over automatically generated text summaries of the testing spoken documents. The Viterbi beam search is guided by a delicate score evaluated from three sets of models: term selection model tells the most suitable terms to be included in the title, term ordering model gives the best ordering of the terms to make the title readable, and title length model tells the reasonable length of the title. The models are trained from a training corpus which is not required to be matched with the testing spoken documents. Both objective evaluation based on F1 measure and subjective human evaluation for relevance and readability indicated the approach is very attractive.

In this paper we propose a set of new methods exploring the topical information embedded in the spoken documents and using such information in automatic summarization of spoken documents. By introducing a set of latent topic variables, Probabilistic Latent Semantic Analysis (PLSA) is useful to find the underlying probabilistic relationships between documents and terms. Two useful measures, referred to as topic significance and term entropy in this paper, are proposed based on the PLSA modeling to determine the terms and thus sentences important for the document which can then be used to construct the summary. Experiment results for preliminary tests performed on broadcast news stories in Mandarin Chinese indicated improved performance as compared to some existing approaches.

In a previous paper [1] two new scoring measures, topic significance (TS) and topic entropy (TE), obtained from probabilistic latent semantic analysis (PLSA) were shown to outperform very successful baseline significance score (SS) in selecting the important sentences for summarization of spoken documents. In this paper extensive experiments using the ROUGE scores with respect to different parameters at different summarization ratios were carefully analyzed in great detail. It was also found that integration of these two scoring measures offered further improvements, and special considerations of the structure of Chinese language was also helpful when summarizing Chinese spoken documents.