NDLI: Deep maxout neural networks for speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Meng Cai Yongzhe Shi Jia Liu
Copyright Year	2013
Description	Author affiliation: Dept. of Electron. Eng., Tsinghua Univ., Beijing, China (Meng Cai; Yongzhe Shi; Jia Liu)
Abstract	A recently introduced type of neural network called maxout has worked well in many domains. In this paper, we propose to apply maxout for acoustic models in speech recognition. The maxout neuron picks the maximum value within a group of linear pieces as its activation. This nonlinearity is a generalization to the rectified nonlinearity and has the ability to approximate any form of activation functions. We apply maxout networks to the Switchboard phone-call transcription task and evaluate the performances under both a 24-hour low-resource condition and a 300-hour core condition. Experimental results demonstrate that maxout networks converge faster, generalize better and are easier to optimize than rectified linear networks and sigmoid networks. Furthermore, experiments show that maxout networks reduce underfitting and are able to achieve good results without dropout training. Under both conditions, maxout networks yield relative improvements of 1.1-5.1% over rectified linear networks and 2.6-14.5% over sigmoid networks on benchmark test sets.
Starting Page	291
Ending Page	296
File Size	701518
Page Count	6
File Format	PDF
ISBN	9781479927562
DOI	10.1109/ASRU.2013.6707745
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-08
Publisher Place	Czech Republic
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Hidden Markov models Speech recognition Training Acoustics Switches Biological neural networks speech recognition Maxout networks acoustic modeling neuron nonlinearity
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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