ARMA lattice model for phoneme feature extraction

Author

Xie, Qing ; Kwan, H.K.

Author_Institution

Dept. of Electr. & Comput. Eng., Windsor Univ., Ont., Canada

fYear

2004

fDate

20-22 Oct. 2004

Firstpage

230

Lastpage

233

Abstract

In this paper, the result of a study on phoneme feature extraction, under a noisy environment, using an auto-regressive moving average (ARMA) lattice model, is presented. The phoneme characteristics are modeled and expressed in the form of ARMA lattice reflection coefficients for classification. Experimental results, based on the TIMIT speech database and NoiseX-92 noise database, indicate that the ARMA lattice model achieves an improved noise-resistant capability on vowel phonemes and fricative phonemes as compared to those of the conventional mel-frequency cepstral coefficient (MFCC) method.

Keywords

autoregressive moving average processes; feature extraction; signal classification; speech recognition; ARMA lattice model; ARMA lattice reflection coefficients; autoregressive moving average model; classification; fricative phonemes; noise database; noise-resistance; noisy environment; phoneme feature extraction; robust speech recognition; speech analysis; speech database; vowel phonemes; Feature extraction; Filters; Lattices; Mel frequency cepstral coefficient; Reflection; Robustness; Speech analysis; Speech recognition; Tiles; Working environment noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Multimedia, Video and Speech Processing, 2004. Proceedings of 2004 International Symposium on

Print_ISBN

0-7803-8687-6

Type

conf

DOI

10.1109/ISIMP.2004.1434042

Filename

1434042