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