研究使用詞彙與語意資訊於

Investigating the Use of Lexical and Semantic Information for Automatic Spoken Document Segmentation and Organization

朱惠銘(Huei-Ming Chu)

指導教授 ： 陳柏琳

國立臺灣師範大學/理學院/資訊工程研究所/碩士(2004年)

摘要

無資料

關鍵字

語音文件切割 ； 語音文件組織 ； 自我組織圖 ； 主題混合模型圖示

並列摘要

Spoken document segmentation is to automatically set the boundaries between different small topics begin mentioned in long steams of audio signals, and divide the spoken documents into a set of cohesive paragraphs of sentences sharing some common central topic. While spoken document organization aims at automatically analyzing the subject topics of the segmented shot paragraphs of the spoken documents, clustering them into groups with topic labels and organizing them into some hierarchical visual presentation easier for users to browse. Both of them have gained growing attention in the past few years.
In the thesis, we explored the use of the Hidden Markov Model (HMM) approach, which has been proven effective for speech recognition and information retrieval, in the context of spoken document segmentation. We not only exploited the lexical information inherent in the spoken document, such as the statistical features or the language model probabilities, but also considered the acoustic information, such as the pause distribution and the confidence measure, in identifying segment boundaries. Moreover, the semantic information conveyed in the spoken document was also integrated into the HMM segmenter for accurately modeling the state observation distributions. On the other hand, we investigated two unsupervised and data-driven organization approaches as well for spoken document analysis, i.e., the Self-Organizing Map (SOM) and Probabilistic Latent Semantic Analysis Map (ProbMap). While for the ProbMap approach, a topical mixture model approach (TMMmap), which came from an alternative perspective, was also studied. A series of experiments was conducted on the Topic Detection and Tracking (TDT) spoken document collections in order to analyze the performance levels of these approaches and compare the differences between them. Finally, we further attempted to incorporate the topic distributions as well as the topological constraints achieved from spoken document organization into the HMM segmenter. Very Promising results were initially demonstrated.

並列關鍵字

Spoken Document Segmentation ； Spoken Document Organization ； Self-Organization Map ； Topic Mixture Model Map

參考文獻

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[陳佳甫, 2003] 陳佳甫, “考慮特徵、語言模型及額外資訊之中文語音文件切割-以廣播新聞為例” 國立台灣大學電信工程學研究所碩士論文, 2003

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