DocumentCode :
630409
Title :
Vocabulary Gaussian Clustering Model Using AELMS Filter
Author :
Jong-Sub Lee ; Sang-Yeob Oh
Author_Institution :
Dept. of Liberal Educ., Semyung Univ., Jecheon, South Korea
fYear :
2013
fDate :
24-26 June 2013
Firstpage :
1
Lastpage :
2
Abstract :
With the AELMS filter, which can preserve sources features of speech and decrease the damage on speech information, noise of a contaminated speech signal got canceled, and a gaussian model was clustered as a method to make noise more robust. By composing a gaussian clustering model, which is a robust speech recognition clustering model, in a noise environment, a recognition performance was evaluated. The study shows that SNR of speech, which was gained by canceling the environment noise which was kept changing, was enhanced by 2.7dB in an average and a recognition rate was improved by 3.1%.
Keywords :
Gaussian processes; adaptive filters; feature extraction; least mean squares methods; pattern clustering; signal denoising; speech recognition; AELMS filter; Gaussian clustering model; SNR; contaminated speech signal noise; least mean square adaptive filter; robust speech recognition clustering model; speech information; speech source feature preservation; vocabulary Gaussian clustering model; Adaptive filters; Hidden Markov models; Noise; Robustness; Speech; Speech processing; Speech recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Applications (ICISA), 2013 International Conference on
Conference_Location :
Suwon
Print_ISBN :
978-1-4799-0602-4
Type :
conf
DOI :
10.1109/ICISA.2013.6579392
Filename :
6579392
Link To Document :
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