NDLI: Learning invariant features for speech separation

Content Provider	IEEE Xplore Digital Library
Author	Kun Han DeLiang Wang
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Ohio State Univ., Columbus, OH, USA (Kun Han; DeLiang Wang)
Abstract	Recent studies on speech separation show that the ideal binary mask (IBM) substantially improves speech intelligibility in noise. Supervised learning can be used to effectively estimate the IBM. However, supervised learning has trouble dealing with the situations where the probabilistic properties of the training data and the test data do not match, resulting in a challenging issue of generalization whereby the system trained under particular noise conditions may not generalize to new noise conditions. We propose to use a novel metric learning method to learn invariant speech features in the kernel space. As the learned features encode speech-related information that is robust to different noise types, the system is expected to generalize to unseen noise conditions. Evaluations show the advantage of the proposed approach over other speech separation systems.
Starting Page	7492
Ending Page	7496
File Size	123064
Page Count	5
File Format	PDF
ISBN	9781479903566
ISSN	15206149
DOI	10.1109/ICASSP.2013.6639119
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise Kernel Speech Support vector machines Measurement Training Feature extraction SVM Speech Separation Domain Adaptation Kernel Learning
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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