DocumentCode
332503
Title
State duration-based segmental probability model
Author
Jia, Bin ; Zhu, Xiaoyan ; Luo, Yuping ; Hu, Dongcheng
Author_Institution
Dept. of Autom., Tsinghua Univ., Beijing, China
Volume
vol.2
fYear
1998
fDate
22-24 Oct 1998
Abstract
This paper suggests a state duration-based segmental probability model (SDSPM) for speech recognition. It comes from incorporating the concept of state duration into the SPM. The distribution of state duration is represented by the bounded gamma distribution (BGD), considered to be better than the gamma distribution. The SDSPM is simpler than the HMM. But the experiments show an average 6% improvement of the rate of recognition accuracy compared with HMM and SPM
Keywords
gamma distribution; speech recognition; bounded gamma distribution; speech recognition; state duration-based segmental probability model; Automation; Computational efficiency; Computer science; Hidden Markov models; Intelligent systems; Laboratories; Probability; Scanning probe microscopy; Speech recognition; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Communication Technology Proceedings, 1998. ICCT '98. 1998 International Conference on
Conference_Location
Beijing
Print_ISBN
7-80090-827-5
Type
conf
DOI
10.1109/ICCT.1998.741434
Filename
741434
Link To Document