Combining LVCSR and vocabulary-independent ranked utterance retrieval for robust speech search

Well tuned Large-Vocabulary Continuous Speech Recognition (LVCSR) has been shown to generally be more effective than vocabulary-independent techniques for ranked retrieval of spoken content when one or the other approach is used alone. Tuning LVCSR systems to a topic domain can be costly, however, and the experiments in this paper show that Out-Of-Vocabulary (OOV) query terms can significantly reduce retrieval effectiveness when that tuning is not performed. Further experiments demonstrate, however, that retrieval effectiveness for queries with OOV terms can be substantially improved by combining evidence from LVCSR with additional evidence from vocabulary-independent Ranked Utterance Retrieval (RUR). The combination is performed by using relevance judgments from held-out topics to learn generic (i.e., topic-independent), smooth, non-decreasing transformations from LVCSR and RUR system scores to probabilities of topical relevance. Evaluated using a CLEF collection that includes top...

• Type: Conference
• Year: 2009
• Where: SIGIR
• Authors: J. Scott Olsson, Douglas W. Oard
• Keywords: Information Retrieval, Large-Vocabulary Continuous Speech, Relevance Judgments, Retrieval Effectiveness, SIGIR 2009