Title :
ARMA lattice model for phoneme feature extraction
Author :
Xie, Qing ; Kwan, H.K.
Author_Institution :
Dept. of Electr. & Comput. Eng., Windsor Univ., Ont., Canada
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;
Conference_Titel :
Intelligent Multimedia, Video and Speech Processing, 2004. Proceedings of 2004 International Symposium on
Print_ISBN :
0-7803-8687-6
DOI :
10.1109/ISIMP.2004.1434042
