DocumentCode :
2333275
Title :
Phonemes as Short Time Cognitive Components
Author :
Feng, Ling ; Hansen, Lars Kai
Author_Institution :
Inf. & Math. Modeling, Tech. Univ. of Denmark
Volume :
5
fYear :
2006
fDate :
14-19 May 2006
Abstract :
Cognitive component analysis (COCA) is defined as the process of unsupervised grouping of data such that the resulting group structure is well-aligned with that resulting from human cognitive activity (L.K. Hansen et al., 2005). In this paper we address COCA in the context short time sound features, finding phonemes which are the smallest contrastive units in the sound system of a language. Generalizable components were found deriving from phonemes based on homomorphic filtering features with basic time scale (20 msec). We sparsified the features based on energy as a preprocessing means to eliminate the intrinsic noise. Independent component analysis was compared with latent semantic indexing, and was demonstrated to be a more appropriate model in COCA
Keywords :
filtering theory; independent component analysis; signal denoising; speech processing; cognitive component analysis; homomorphic filtering features; human cognitive activity; independent component analysis; intrinsic noise elimination; latent semantic indexing; phoneme contrastive units; short time cognitive components; short time sound features; unsupervised data grouping; Audio systems; Filtering; Humans; Independent component analysis; Indexing; Large scale integration; Matrix decomposition; Signal analysis; Signal processing; Source separation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
Conference_Location :
Toulouse
ISSN :
1520-6149
Print_ISBN :
1-4244-0469-X
Type :
conf
DOI :
10.1109/ICASSP.2006.1661414
Filename :
1661414
Link To Document :
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