Phonemes as short time cognitive components

Ling Feng, Lars Kai Hansen

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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 languageEnglish
    Title of host publicationInternational Conference on Acoustics, Speech and Signal Processing (ICASSP'06)
    Publication date2006
    Pages869-872
    Publication statusPublished - 2006
    EventIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006) - Toulouse, France
    Duration: 14 May 200619 May 2006

    Conference

    ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)
    CountryFrance
    CityToulouse
    Period14/05/200619/05/2006

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