| 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 (1997) |
| 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 |
