Report Number: CS-TR-73-385
Institution: Stanford University, Department of Computer Science
Title: Recognition of continuous speech: segmentation and
classification using signature table adaptation.
Author: Thosar, Ravindra B.
Date: September 1973
Abstract: This report explores the possibility of using a set of
features for segmentation and recognition of continuous
speech. The features are not necessarily "distinctive" or
minimal, in the sense that they do not divide the phonemes
into mutually exclusive subsets, and can have high
redundancy. This concept of feature can thus avoid apriori
binding between the phoneme categories to be recognized and
the set of features defined in a particular system.
An adaptive technique is used to find the probability of the
presence of a feature. Each feature is treated independently
of other features. An unknown utterance is thus represented
by a feature graph with associated probabilities. It is hoped
that such a representation would be valuable for a
hypothesize-test paradigm as opposed to one which operates on
a linear symbolic input.