Unsupervised spoken keyword spotting via segmental DTW on Gaussian posteriorgrams
Name
Yaodong-2009-Unsupervised spoken keyword spotting via segmental DTW on Gaussian posteriorgrams.pdf
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118.7 KB
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Author(s)
Glass, James R.
Zhang, Yaodong, Ph. D. Massachusetts Institute of Technology
Date Issued
January 2010
Journal
IEEE Workshop on Automatic Speech Recognition & Understanding, 2009 (ASRU 2009)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Zhang, Yaodong, and James R. Glass. “Unsupervised Spoken Keyword Spotting via Segmental DTW on Gaussian Posteriorgrams.” Proceedings of the 2009 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU 2009) IEEE, 2009. 398–403. (c) 2009 IEEE
Version
Final published version
Abstract
In this paper, we present an unsupervised learning framework to address the problem of detecting spoken keywords. Without any transcription information, a Gaussian Mixture Model is trained to label speech frames with a Gaussian posteriorgram. Given one or more spoken examples of a keyword, we use segmental dynamic time warping to compare the Gaussian posteriorgrams between keyword samples and test utterances. The keyword detection result is then obtained by ranking the distortion scores of all the test utterances. We examine the TIMIT corpus as a development set to tune the parameters in our system, and the MIT Lecture corpus for more substantial evaluation. The results demonstrate the viability and effectiveness of our unsupervised learning framework on the keyword spotting task.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1109/ASRU.2009.5372931
