Please use this identifier to cite or link to this item: https://doi.org/10.1145/1658377.1658379
Title: Statistical lattice-based spoken document retrieval
Authors: Chia, T.K. 
Sim, K.C.
Li, H.
Ng, H.T. 
Keywords: Lattice-based spoken document retrieval
Probabilistic retrieval approach
Retrieval of conversational speech
Issue Date: 2010
Source: Chia, T.K., Sim, K.C., Li, H., Ng, H.T. (2010). Statistical lattice-based spoken document retrieval. ACM Transactions on Information Systems 28 (1). ScholarBank@NUS Repository. https://doi.org/10.1145/1658377.1658379
Abstract: Recent research efforts on spoken document retrieval have tried to overcome the low quality of 1-best automatic speech recognition transcripts, especially in the case of conversational speech, by using statistics derived from speech lattices containing multiple transcription hypotheses as output by a speech recognizer. We present a method for lattice-based spoken document retrieval based on a statistical n-gram modeling approach to information retrieval. In this statistical lattice-based retrieval (SLBR) method, a smoothed statistical model is estimated for each document from the expected counts of words given the information in a lattice, and the relevance of each document to a query is measured as a probability under such a model.We investigate the efficacy of our method under various parameter settings of the speech recognition and lattice processing engines, using the Fisher English Corpus of conversational telephone speech. Experimental results show that our method consistently achieves better retrieval performance than using only the 1-best transcripts in statistical retrieval, outperforms a recently proposed lattice-based vector space retrieval method, and also compares favorably with a lattice-based retrieval method based on the Okapi BM25 model. © 2010 ACM 1046-8188/2010/01-ART1 $10.00.
Source Title: ACM Transactions on Information Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/39758
ISSN: 10468188
DOI: 10.1145/1658377.1658379
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