Ling Feng, Lars Kai Hansen

AbstractCognitive 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.
KeywordsPhonemes, Cognitive Component Analysis, Independent Component Analysis, Latent Semantic Indexing
TypeConference paper [With referee]
ConferenceInternational Conference on Acoustics, Speech and Signal Processing (ICASSP'06)
Year2006    Month May    Vol. V    pp. 869-872
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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