Description

Huge, continually increasing quantities of multimedia content including speech information are filling up our computers, networks and lives. It is obvious that speech is one of the most important sources of information for multimedia con-tent, as it is the speech of the content that tells us of the subjects, topics and concepts. This study presents a novel approach to spoken document retrieval based on Markov Decision Process. It considers three information sources, namely transcription data, keywords extracted from spoken documents, and hyponyms of the extracted keywords. Construct a key term hierarchy for efficient document retrieval. The system returns not only the retrieval results but also a short list of key terms describing distinct topics. The user selects these key terms to expand the query if the retrieval results are not satisfactory. The entire retrieval process is organized around a hierarchy of key terms that define the allowable state transitions. By reinforcement learning with simulated users, the key terms on the short list are properly ranked such that the retrieval success rate is maximized while the number of interactive steps is minimized. Significant improvements over existing approaches were observed in preliminary experiments performed on information needs provided by real users.