@CONFERENCE\{IMM2006-04058,
    author       = "L. Feng and L. K. Hansen",
    title        = "{PHONEMES} {AS} {SHORT} {TIME} {COGNITIVE} {COMPONENTS}",
    year         = "2006",
    month        = "may",
    keywords     = "Phonemes, Cognitive Component Analysis, Independent Component Analysis, Latent Semantic Indexing",
    pages        = "869-872",
    booktitle    = "International Conference on Acoustics, Speech and Signal Processing (ICASSP'06)",
    volume       = "V",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/4058-full.html",
    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}."
}