NDLI: Acoustic modeling with neural graph embeddings

Content Provider	IEEE Xplore Digital Library
Author	Liu, Yuzong Kirchhoff, Katrin
Copyright Year	2015
Description	Author affiliation: Department of Electrical Engineering, University of Washington, Seattle, WA 98195 (Liu, Yuzong; Kirchhoff, Katrin)
Abstract	Graph-based learning (GBL) is a form of semi-supervised learning that has been successfully exploited in acoustic modeling in the past. It utilizes manifold information in speech data that is represented as a joint similarity graph over training and test samples. Typically, GBL is used at the output level of an acoustic classifier; however, this setup is difficult to scale to large data sets, and the graph-based learner is not optimized jointly with other components of the speech recognition system. In this paper we explore a different approach where the similarity graph is first embedded into continuous space using a neural autoencoder. Features derived from this encoding are then used at the input level to a standard DNN-based speech recognizer. We demonstrate improved scalability and performance compared to the standard GBL approach as well as significant improvements in word error rate on a medium-vocabulary Switchboard task.
Starting Page	581
Ending Page	588
File Size	520914
Page Count	8
File Format	PDF
e-ISBN	9781479972913
DOI	10.1109/ASRU.2015.7404848
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Acoustics Hidden Markov models Training Standards Data models Error analysis Encoding graph-based learning Acoustic modeling deep neural networks
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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