Castsearch - Context Based Spoken Document Retrieval

Lasse Lohilahti Mølgaard, Kasper Winther Jørgensen, Lars Kai Hansen

AbstractThe paper describes our work on the development of a system for retrieval of relevant stories from broadcast news. The system utilizes a combination of audio processing and text mining. The audio processing consists of a segmentation step that partitions the audio into speech and music. The speech is further segmented into speaker segments and then transcribed using an automatic speech recognition system, to yield text input for clustering using non-negative matrix factorization (NMF). We find semantic topics that are used to evaluate the performance for topic detection. Based on these topics we show that a novel query expansion can be performed to return more intelligent search results. We also show that the query expansion helps overcome errors inferred by the automatic transcription.
KeywordsAudio Retrieval, Document Clustering, Nonnegative Matrix Factorization, Text Mining
TypeConference paper [With referee]
Year2007    Month April
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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