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