NDLI: Learning the Intrinsic Dimensions of the Timit Speech Database with Maximum Variance Unfolding

Content Provider	IEEE Xplore Digital Library
Author	Vasiloglou, N. Anderson, D.V. Gray, A.G.
Copyright Year	2009
Description	Author affiliation: Georgia Institute of Technology, Atlanta GA 30332, agray@cc.gatech.edu (Gray, A.G.) \|\| Georgia Institute of Technology, Atlanta GA 30332, dva@ece.gatech.edu (Anderson, D.V.) \|\| Georgia Institute of Technology, Atlanta GA 30332, nvasil@ieee.org (Vasiloglou, N.)
Abstract	Modern methods for nonlinear dimensionality reduction have been used extensively in the machine learning community for discovering the intrinsic dimension of several datasets. In this paper we apply one of the most successful ones Maximum Variance Unfolding on a big sample of the well known speech benchmark TIMIT. Although MVU is not generally scalable, we managed to apply to 1 million 39-dimensional points and successfully reduced the dimension down to 15. In this paper we apply some of the state-of-the-art techniques for handling big datasets. The biggest bottleneck is the local neighborhood computation. For 300K points it took 9 hours while for 1M points it took 3.5 days. We also demonstrate the weakness of MFCC representation under the k-nearest neighborhood classification since the error rate is more than 50%.
Starting Page	48
Ending Page	53
File Size	523361
Page Count	6
File Format	PDF
ISBN	9781424436774
DOI	10.1109/DSP.2009.4785894
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-01-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Databases Speech recognition Machine learning Mel frequency cepstral coefficient Cepstrum Testing Manifolds Error analysis Search engines Smoothing methods Mel Cepstrum Maximum Variance Unfolding Manifold Learning Dimensionality Reduction Speech
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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