NDLI: Multi-keyword spotting based on speech feature space trace matching

Content Provider	IEEE Xplore Digital Library
Author	Feng-Qin Li Ya-Dong Wu
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Shanghai Jiao Tong Univ., China (Feng-Qin Li; Ya-Dong Wu)
Abstract	Keyword spotting has been an active research issue in recent years. An efficient multi-keyword spotting method based on the speech feature space trace matching is proposed. Let each template do the moving matching along test speech trace and take down each step's matched score. If the score is lower than the template threshold, write the matched score to the distance matrix. In consideration during the spotting, once some template matched successfully the next matching template is on the left part or the right part of the previous matched template, so the bi-tree recursively-created algorithm is adopted. Using this algorithm, each time find a lowest matched score in the distance matrix to create the bi-tree node, and then output the bi-tree, which is the spotting result. Compared to other methods, this method can greatly reduce the computation in spotting. Experiment shows the recognition rate for speaker-independent is 90.8% and for speaker-dependent is 97.8%, having considerable practicability.
Starting Page	3542
Ending Page	3546
File Size	295801
Page Count	5
File Format	PDF
ISBN	0780384032
DOI	10.1109/ICMLC.2004.1380403
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-08-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Testing Speech recognition Neural networks Computer science Background noise Vocabulary Monitoring Machine learning Mathematics
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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