NDLI: An extreme learning machine approach for speaker recognition

Content Provider	Springer Nature Link
Author	Lan, Yuan Hu, Zongjiang Soh, Yeng Chai Huang, Guang Bin
Copyright Year	2012
Abstract	Over the last two decades, automatic speaker recognition has been an interesting and challenging problem to speech researchers. It can be classified into two different categories, speaker identification and speaker verification. In this paper, a new classifier, extreme learning machine, is examined on the text-independent speaker verification task and compared with SVM classifier. Extreme learning machine (ELM) classifiers have been proposed for generalized single hidden layer feedforward networks with a wide variety of hidden nodes. They are extremely fast in learning and perform well on many artificial and real regression and classification applications. The database used to evaluate the ELM and SVM classifiers is ELSDSR corpus, and the Mel-frequency Cepstral Coefficients were extracted and used as the input to the classifiers. Empirical studies have shown that ELM classifiers and its variants could perform better than SVM classifiers on the dataset provided with less training time.
Starting Page	417
Ending Page	425
Page Count	9
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	22
Issue Number	3-4
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-05-19
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Speaker verification Extreme learning machine Optimization method based extreme learning machine Regularized extreme learning machine Kernelized extreme learning machine Support vector machine Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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