Machine Learning Through Signature Trees. Applications to Human Speech. AIM-136

Signature tree "machine learning", pattern recognition heuristics are
investigated for the specific problem of computer recognition of
human speech. When the data base of given utterances is insufficient
to establish trends with confidence, a large number of feature
extractors must be employed and "recognition" of an unknown pattern
made by comparing its feature values with those of known patterns.
When the data base is replete, a "signature" tree can be constructed
and recognition can be achieved by the evaluation of a select few
features. Learning results from selecting an optimal minimal set of
features to achieve recognition. Properties of signature trees and
the heuristics for this type of learning are of primary interest in
this exposition.