Phonemes as short time cognitive components

Ling Feng; Lars Kai Hansen

Phonemes as short time cognitive components

Ling Feng
, Lars Kai Hansen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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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. In this paper we address COCA in the context short time sound features, finding phonemes which are the smallest contrastive unit 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.

Original language	English
Title of host publication	International Conference on Acoustics, Speech and Signal Processing (ICASSP'06)
Publication date	2006
Pages	869-872
Publication status	Published - 2006
Event	2006 IEEE International Conference on Acoustics, Speech and Signal Processing - Toulouse, France Duration: 14 May 2006 → 19 May 2006 Conference number: 31

Conference

Conference	2006 IEEE International Conference on Acoustics, Speech and Signal Processing
Number	31
Country/Territory	France
City	Toulouse
Period	14/05/2006 → 19/05/2006

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title = "Phonemes as short time cognitive components",

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. In this paper we address COCA in the context short time sound features, finding phonemes which are the smallest contrastive unit 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 = "Latent Semantic Indexing, Independent Component Analysis, Cognitive Component Analysis, Phonemes",

author = "Ling Feng and Hansen, \{Lars Kai\}",

year = "2006",

language = "English",

pages = "869--872",

booktitle = "International Conference on Acoustics, Speech and Signal Processing (ICASSP'06)",

note = "2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 ; Conference date: 14-05-2006 Through 19-05-2006",

}

TY - GEN

T1 - Phonemes as short time cognitive components

AU - Feng, Ling

AU - Hansen, Lars Kai

N1 - Conference code: 31

PY - 2006

Y1 - 2006

N2 - 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. In this paper we address COCA in the context short time sound features, finding phonemes which are the smallest contrastive unit 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.

AB - 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. In this paper we address COCA in the context short time sound features, finding phonemes which are the smallest contrastive unit 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.

KW - Latent Semantic Indexing

KW - Independent Component Analysis

KW - Cognitive Component Analysis

KW - Phonemes

M3 - Article in proceedings

SP - 869

EP - 872

BT - International Conference on Acoustics, Speech and Signal Processing (ICASSP'06)

T2 - 2006 IEEE International Conference on Acoustics, Speech and Signal Processing

Y2 - 14 May 2006 through 19 May 2006

ER -