On Phonemes as Cognitive Components of Speech

Ling Feng, Lars Kai Hansen

AbstractCOgnitive Component Analysis (COCA) 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, has been explored on phoneme data. Statistical regularities have been revealed at multiple time scales. The basic features are 25-dimensional short time (20ms) melfrequency
weighted cepstral coefficients. Features are integrated by means of stacking to obtain features at longer time scales. Energy based sparsification is carried out to achieve sparse representations. Our hypothesis is ecological: we assume that features that essentially independent in a context defined ensemble can be efficiently coded using a sparse independent component representation. This means that supervised and unsupervised learning should result in similar representations. We indeed find that supervised and supervised
learning seem to identify similar representations, here, measured by the classification similarity.
KeywordsCognitive Component Analysis, Unsupervised Learning, Supervised Learning, Phoneme Classification
TypeConference paper [With referee]
ConferenceThe 1st IAPR Workshop on Cognitive Information Processing
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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