Structure and content-based segmentation of speech transcripts
D. Ponceleon, S. Srinivasan
SIGIR Forum (ACM Special Interest Group on Information Retrieval)
Combined word-based indexes and phonetic indexes have been used to improve the performance of spoken document retrieval systems primarily by addressing the out-of-vocabulary retrieval problem. However, a known problem with phonetic recognition is its limited accuracy in comparison with word level recognition. We propose a novel method for phonetic retrieval in the CueVideo system based on the probabilistic formulation of term weighting using phone confusion data in a Bayesian framework. We evaluate this method of spoken document retrieval against word-based retrieval for the search levels identified in a realistic video-based distributed learning setting. Using our test data, we achieved an average recall of 0.88 with an average precision of 0.69 for retrieval of out-of-vocabulary words on phonetic transcripts with 35% word error rate. For in-vocabulary words, we achieved a 17% improvement in recall over word-based retrieval with a 17% loss in precision for word error rates ranging from 35 to 65%.
D. Ponceleon, S. Srinivasan
SIGIR Forum (ACM Special Interest Group on Information Retrieval)
A. Amir, D. Ponceleon, et al.
HICSS 2000
D. Petkovic, J. Sanz, et al.
Computer Architecture for Pattern Analysis and Image Database Management 1984
W.E. Blanz, B. Shung, et al.
ICPR 1990