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Interpolation of Estimators Derived from Sparse Data

IP.com Disclosure Number: IPCOM000053225D
Original Publication Date: 1981-Sep-01
Included in the Prior Art Database: 2005-Feb-12

Publishing Venue

IBM

Related People

Authors:
Bahl, LR Jelinek, F Mercer, RL [+details]

Abstract

An estimation technique is described for building statistical speech models wherein improved estimators are calculated from sparse data in the speech recognition application. Markov sources are a widely used class of statistical models of generators of data. A Markov source consists of a set of states S, transitions T between them, and outputs from alphabet A associated with the transitions. At given time instants, the source changes state from s to s' along some transition t (with probability q(s)(t)), and outputs the letter a associated with t. The usual modeling approach is to determine (by intuition, physical reasoning, etc.) the structure S, T, A of the source and then use sample training data to estimate the transition probabilities q(s)(t). An efficient iterative method of estimation due to Baum [1] exists.