Show Reference: "Phonetic Feature Discovery in Speech Using Snap-Drift Learning Artificial Neural Networks"

Phonetic Feature Discovery in Speech Using Snap-Drift Learning Artificial Neural Networks Vol. 4132 (2006), pp. 952-962, doi:10.1007/11840930_99 by Sin Lee, Dominic Palmer-Brown edited by Stefanos Kollias, Andreas Stafylopatis, Wlodzislaw Duch, Erkki Oja
@incollection{lee-and-palmer-brown-2006,
    abstract = {This paper presents a new application of the snap-drift algorithm [1]: feature discovery and clustering of speech waveforms from non-stammering and stammering speakers. The learning algorithm is an unsupervised version of snap-drift which employs the complementary concepts of fast, minimalist learning ( snap ) \& slow drift (towards the input pattern) learning. The {Snap-Drift} Neural Network ({SDNN}) is toggled between snap and drift modes on successive epochs. The speech waveforms are drawn from a phonetically annotated corpus, which facilitates phonetic interpretation of the classes of patterns discovered by the {SDNN}.},
    address = {Berlin, Heidelberg},
    author = {Lee, Sin and Palmer-Brown, Dominic},
    chapter = {99},
    doi = {10.1007/11840930\_99},
    editor = {Kollias, Stefanos and Stafylopatis, Andreas and Duch, Wlodzislaw and Oja, Erkki},
    isbn = {978-3-540-38871-5},
    keywords = {ann, learning},
    pages = {952--962},
    posted-at = {2011-10-31 16:56:10},
    priority = {1},
    publisher = {Springer Berlin / Heidelberg},
    series = {Lecture Notes in Computer Science},
    title = {Phonetic Feature Discovery in Speech Using {Snap-Drift} Learning Artificial Neural Networks},
    url = {http://dx.doi.org/10.1007/11840930\_99},
    volume = {4132},
    year = {2006}
}

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