語音文件檢索(Spoken Document Retrieval)在語音處理研究領域一直是令人感興趣的研究題目。語音文件檢索的研究常面臨的問題可歸納成三大層面：(1)通常查詢(Query)傴是使用者資訊需求(Information Need)的一種用較含糊的表達方式，並不能完整代表使用者資訊需求所欲表達的語意；(2)在語音文件與使用者查詢中常會使用不同的詞彙來表相同的主題或概念(Topic or Concept)；(3)語音文件經自動語音辨識(Automatic Speech Recognition, ASR)轉寫成文字時，常受限於語音辨識之正確率，而導致資訊檢索效能的降低。基於上述觀察，本論文提出許多查詢模型化(Query Modeling)改進方式，用以減輕語音文件檢索面臨的問題。未達此目的，吾人嘗詴探索關聯性語言模型(Relevance Language Model)於語音文件檢
索之使用；同時， 吾人在此模型架構中融入了文件層次主題資訊(Topic
Information)與查詢非相關資訊(Non-relevance Information)，以期增進查詢模型化之效果。本論文的實驗是進行在國際廣泛使用的Topic Detection and Tracking(TDT)語料庫；實驗結果顯示吾人所提出之檢索方法，相較於一些現有檢索方法，能達到更好的檢索效能。

Spoken document retrieval (SDR) has recently become a more interesting research avenue due to increasing volumes of publicly available multimedia associated with speech information. The fundamental problems facing SDR are generally three-fold: 1) a query is often only a vague expression of an underlying information need, 2) there
probably would be word usage mismatch between a query and a spoken document even if they are topically related to each other, and 3) the imperfect speech recognition transcript carries wrong information and thus deviates somewhat from
representing the true theme of a spoken document. Many efforts have been devoted to developing elaborate indexing and modeling techniques for representing spoken documents, but few to improving query formulations for better representating the users‟ information needs. In view of this, we presented a novel language modeling framework exploring both lexical- and topic-based relevance formation for improving query effectiveness. We further explore various ways to glean both relevance and non-relevance information from the document collection so as to enhance the modeling of a given query in an unsupervised fashion. Experiments conducted on the TDT (Topic Detection and Tracking) SDR task demonstrate the perofrmance merits of the methods deduced from our retrieval framework deliver
when compared to other existing retrieval methods.

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