Phonetic confusion matrix based spoken document retrieval
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Detecting topical events in digital video
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Toward speech as a knowledge resource
IBM Systems Journal
Maximum entropy model-based baseball highlight detection and classification
Computer Vision and Image Understanding - Special issue on event detection in video
Locating thematic pinpoints in narrative texts with short phrases: a test study on Don Quixote
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Search the audio, browse the video: a generic paradigm for video collections
EURASIP Journal on Applied Signal Processing
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Advances in speech recognition technology have shown encouraging results for spoken document retrieval where the average precision often approaches 70% of that achieved for perfect text transcriptions. Typical applications of spoken document retrieval pertain to retrieval of stories from archived video/audio assets. In the CueVideo project, our application focus is spoken document retrieval from a video database for just-in-time training/distributed learning. Typical content is not pre-segmented, has no predefined structure, is of varying audio quality, and may not have domain specific data available. For such content, we propose a two level search, namely, a first level search across the entire video collection, and a second level search within a specific video. At both search levels, we perform an experimental evaluation of a combination of new and existing query expansion methods, intended to offset retrieval errors due to misrecognition.