DocumentCode
3098925
Title
An Effective Method to Decrease the Dimension of Input Vector of BPNN on ASR System
Author
Huang, Wan-chen
Author_Institution
Wu-Feng Inst. of Technol., Chiayi
fYear
2007
fDate
5-8 Nov. 2007
Firstpage
2499
Lastpage
2502
Abstract
This paper uses BP neural network for modeling and recognition in the ASR (automatic speech recognition system) to get a high performance. But it still has some disadvantages, one of which is that it needs to construct a high dimension of input vector, so it will waste a lot of memory storage and spend much time in computing. In this paper we present a new method to combine HMM and BPNN to decrease the dimension of input vector and still keep a high recognition rate while recognizing.
Keywords
backpropagation; hidden Markov models; neural nets; speech recognition; automatic speech recognition system; backpropagation neural network; hidden Markov models; memory storage; Automatic speech recognition; Hidden Markov models; Industrial Electronics Society; Mechanical engineering; Neural networks; Notice of Violation; Probability distribution; Real time systems; Speech recognition; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Electronics Society, 2007. IECON 2007. 33rd Annual Conference of the IEEE
Conference_Location
Taipei
ISSN
1553-572X
Print_ISBN
1-4244-0783-4
Type
conf
DOI
10.1109/IECON.2007.4460202
Filename
4460202
Link To Document