mirage

HMM speech recognition with reduced training

DSpace/Manakin Repository

 

Search DR-NTU


Advanced Search Subject Search

Browse

My Account

HMM speech recognition with reduced training

Show simple item record

dc.contributor.author Foo, Say Wei
dc.contributor.author Yap, Timothy
dc.date.accessioned 2009-07-21T08:15:03Z
dc.date.available 2009-07-21T08:15:03Z
dc.date.copyright 1997
dc.date.issued 2009-07-21T08:15:03Z
dc.identifier.citation Foo, S. W. & Yap, T. (1997). HMM speech recognition with reduced training. Proceedings of the International Conference on Information, Communications and Signal Processing 1997: (pp. 1016-1019).
dc.identifier.uri http://hdl.handle.net/10220/4687
dc.description.abstract One of the problems faced in automatic speech recognition is the amount of training required to adapt the machine to the speaker way of pronunciation. To a certain extent, the accuracy of correct recognition is proportional to the amount of training and adaptation carried out. This is especially true when a large vocabulary is involved. For cerlain applications, it is desirable that the training requirement be reduced to the bare minimum without sacrificing the accuracy of recognition. In this paper, the minimum number of training required to achieve an acceptable degree of accuracy for a speaker dependent speech recognition system based on the Hidden Markov Model (HMM) is investigated. A method is also proposed which retains the same degree of accuracy of recognition with much reduced training.
dc.format.extent 4 p.
dc.language.iso en
dc.rights © 1997 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
dc.subject DRNTU::Engineering::Electrical and electronic engineering::Electronic circuits
dc.title HMM speech recognition with reduced training
dc.type Conference Paper
dc.contributor.conference IEEE International Conference on Information, Communications and Signal Processing (1st : 1997 : Singapore)
dc.contributor.school School of Electrical and Electronic Engineering
dc.identifier.doi http://dx.doi.org/10.1109/ICICS.1997.652134
dc.description.version Published version

Files in this item

Files Size Format View Description
C16-00652134-ICICS1997TimothyYap.pdf 312.5Kb PDF View/Open Published version

This item appears in the following Collection(s)

Show simple item record

Statistics

Total views

All Items Views
HMM speech recognition with reduced training 406

Total downloads

All Bitstreams Views
C16-00652134-ICICS1997TimothyYap.pdf 273

Top country downloads

Country Code Views
China 90
United States of America 89
Singapore 51
Indonesia 7
Malaysia 5

Top city downloads

city Views
Mountain View 57
Singapore 51
Beijing 39
Seattle 5
Redwood City 4

Downloads / month

  2014-06 2014-07 2014-08 total
C16-00652134-ICICS1997TimothyYap.pdf 0 0 12 12