NDLI: Speech pattern classification using Large Geometric Margin Minimum Classification Error training

Content Provider	IEEE Xplore Digital Library
Author	Kitaoka, M. Hashimoto, T. Ochiai, T. Katagiri, S. Ohsaki, M. Watanabe, H. Xugang Lu Kawai, H.
Copyright Year	2015
Description	Author affiliation: Nat. Inst. of Inf. & Commun. Technol., Kyoto, Japan (Watanabe, H.; Xugang Lu; Kawai, H.) \|\| Doshisha Univ., Kyoto, Japan (Kitaoka, M.; Hashimoto, T.; Ochiai, T.; Katagiri, S.; Ohsaki, M.)
Abstract	As one of the recent popular discriminative training methods, Minimum Classification Error (MCE) training aims at efficiently developing high-performance classifiers through the minimization of smooth (differentiable in classifier parameters) classification error count loss. However, MCE training, sometimes referred to as Functional Margin (FM) MCE training, does not necessarily guarantee training convergence to a high level of robustness. To solve this problem, a new version of MCE training, called Large Geometric Margin Minimum Classification Error (LGM-MCE) training, has recently been developed by introducing a geometric margin for a general form of discriminant functions for fixed-dimensional vector pattern samples. Its effectiveness in achieving robustness to unseen samples has been proven in various tasks that classify fixed-dimensional patterns. Leveraging this advance in MCE training formalization, we newly define LGM-MCE training for the classification of patterns of variable length, e.g. speech patterns, and demonstrate this training's effectiveness in a spoken-word classification task.
Starting Page	1
Ending Page	6
File Size	129401
Page Count	6
File Format	PDF
ISBN	9781479986392
ISSN	21593450
e-ISBN	9781479986415
DOI	10.1109/TENCON.2015.7372850
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Prototypes Speech Indexes Minimization Robustness Optimization Speech pattern classification Minimum classification error training Large geometric margin minimum classification error training
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Minimum Error Classification with geometric margin control

Automatic loss smoothness determination for Large Geometric Margin Minimum Classification Error training

Minimum classification error training with geometric margin enhancement for robust pattern recognition

Experimental evaluation of Kernel Minimum Classification Error training

Sample-separation-margin based minimum classification error training of pattern classifiers with quadratic discriminant functions

Robust and Efficient Pattern Classification using Large Geometric Margin Minimum Classification Error Training

Minimum Classification Error training employing Real-Coded Genetic Algorithms

Discriminative Training for Large-Vocabulary Speech Recognition Using Minimum Classification Error

Large-margin minimum classification error training for large-scale speech recognition tasks

Speech pattern classification using Large Geometric Margin Minimum Classification Error training

Similar Documents

Minimum Error Classification with geometric margin control

Automatic loss smoothness determination for Large Geometric Margin Minimum Classification Error training

Minimum classification error training with geometric margin enhancement for robust pattern recognition

Experimental evaluation of Kernel Minimum Classification Error training

Sample-separation-margin based minimum classification error training of pattern classifiers with quadratic discriminant functions

Robust and Efficient Pattern Classification using Large Geometric Margin Minimum Classification Error Training

Minimum Classification Error training employing Real-Coded Genetic Algorithms

Discriminative Training for Large-Vocabulary Speech Recognition Using Minimum Classification Error

Large-margin minimum classification error training for large-scale speech recognition tasks

Speech pattern classification using Large Geometric Margin Minimum Classification Error training