Is Cognitive Activity of Speech Based On Statistical Independence?

Ling Feng, Lars Kai Hansen

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    This paper explores the generality of COgnitive Component Analysis (COCA), which is defined as the process of unsupervised grouping of data such that the ensuing group structure is well-aligned with that resulting from human cognitive activity. The hypothesis of {COCA} is ecological: the essentially independent features in a context defined ensemble can be efficiently coded using a sparse independent component representation. Our devised protocol aims at comparing the performance of supervised learning (invoking cognitive activity) and unsupervised learning (statistical regularities) based on similar representations, and the only difference lies in the human inferred labels. Inspired by the previous research on COCA, we introduce a new pair of models, which directly employ the independent hypothesis. Statistical regularities are revealed at multiple time scales on phoneme, gender, age and speaker identity derived from speech signals. We indeed find that the supervised and unsupervised learning provide similar representations measured by the classification similarity at different levels.
    Original languageEnglish
    Title of host publicationProccedings of the 30th Meeting of the Cognitive Science Society (CogSci'08)
    PublisherCognitive Science Society
    Publication date2008
    ISBN (Print)978-0-9768318-3-9
    Publication statusPublished - 2008
    EventMeeting of the Cognitive Science Society - Washington D.C.
    Duration: 1 Jan 2008 → …
    Conference number: 30


    ConferenceMeeting of the Cognitive Science Society
    CityWashington D.C.
    Period01/01/2008 → …


    • statistical regularity
    • classification
    • unsupervised learning
    • Cognitive component analysis
    • supervised learning


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