Application of symbolic machine learning to audio signal segmentation
Nonlinear Speech Modeling and Applications
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Designed for an operational prospect, the CHARADE system automatically learns consistent rule systems from a description language, a set of axioms reflecting the language semantics and a set of examples. The technique advocated below is based on a "generate and test" mechanism where the description space is explored from the more general to the more specific descriptions. Rules and properties to be obtained are translated into exploration procedure constraints thanks to formalization of the learning set with two Boolean lattices.The underlying theoretical framework allows to both justify the heuristics conventionnaly used similarity based-learning and to introduce global properties to be satisfied by a rule system during its construction.