Is Cognitive Activity of Speech Based On Statistical Independence?

Ling Feng, Lars Kai Hansen

AbstractThis 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.
KeywordsCognitive component analysis; statistical regularity;unsupervised learning; supervised learning; classification.
TypeConference paper [With referee]
ConferenceCogSci 2008 - 30th Annual Meeting of the Cognitive Science Society
Year2008
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing


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