Keyword spotting based on mixed grammar model

Author

Yining, Chen ; Jing, Liu ; Lin, Zhong ; Jia, Liu ; Runsheng, Liu

Author_Institution

Dept. of Electron. Eng., Tsinghua Univ., Beijing, China

fYear

2001

fDate

2001

Firstpage

425

Lastpage

428

Abstract

We present a novel keyword spotting method based on the mixed grammar model. By merging the filler model and the finite state grammar, two conventional technologies of keyword spotting, the mixed grammar model incorporates both a priori knowledge and the capability of covering all possible sentential forms in real speech, thus makes up for the weaknesses of both parental technologies. Experimental results show that the mixed grammar model excels the filler model in overall performance and the finite state grammar in robustness. The expansibility of the mixed grammar model is shown in its capacity of easy incorporation of further improvement of both the filler model and finite state grammar

Keywords

grammars; speech recognition; experimental results; filler model; finite state grammar; keyword spotting method; mixed grammar model; speech recognition; Assembly; Automatic speech recognition; Buildings; Decoding; Hidden Markov models; Information systems; Intelligent structures; Merging; Robustness; Speech processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Multimedia, Video and Speech Processing, 2001. Proceedings of 2001 International Symposium on

Conference_Location

Hong Kong

Print_ISBN

962-85766-2-3

Type

conf

DOI

10.1109/ISIMP.2001.925424

Filename

925424