NDLI: Posterior-based confidence measures for spoken term detection

Content Provider	IEEE Xplore Digital Library
Author	Dong Wang Tejedor, J. Frankel, J. King, S. Colas, J.
Copyright Year	2009
Description	Author affiliation: The Centre for Speech Technology Research, University of Edinburgh, UK (Dong Wang; Tejedor, J.; Frankel, J.; King, S.) \|\| Human Computer Technology Laboratory, Escuela Politecnica Superior, Universidad Autonoma de Madrid, Spain (Colas, J.)
Abstract	Confidence measures play a key role in spoken term detection (STD) tasks. The confidence measure expresses the posterior probability of the search term appearing in the detection period, given the speech. Traditional approaches are based on the acoustic and language model scores for candidate detections found using automatic speech recognition, with Bayes' rule being used to compute the desired posterior probability. In this paper, we present a novel direct posterior-based confidence measure which, instead of resorting to the Bayesian formula, calculates posterior probabilities from a multi-layer perceptron (MLP) directly. Compared with traditional Bayesian-based methods, the direct-posterior approach is conceptually and mathematically simpler. Moreover, the MLP-based model does not require assumptions to be made about the acoustic features such as their statistical distribution and the independence of static and dynamic co-efficients. Our experimental results in both English and Spanish demonstrate that the proposed direct posterior-based confidence improves STD performance.
Starting Page	4889
Ending Page	4892
File Size	141038
Page Count	4
File Format	PDF
ISBN	9781424423538
ISSN	15206149
DOI	10.1109/ICASSP.2009.4960727
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-19
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Acoustic measurements Acoustic signal detection Bayesian methods Lattices Speech recognition Probability Hidden Markov models Humans Laboratories Natural languages MLP Spoken term detection confidence measure posterior probabilities
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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