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Fast Algorithm for Computing the Optimal Linear Combination of Probability Distributions

IP.com Disclosure Number: IPCOM000062214D
Original Publication Date: 1986-Oct-01
Included in the Prior Art Database: 2005-Mar-09
Document File: 1 page(s) / 12K

Publishing Venue

IBM

Related People

Bahl, LR: AUTHOR [+5]

Abstract

The present invention relates to an algorithm which, given distributions P(.) and Q(.) and a sample output X, is directed to computing the optimal linear combination gP(.) + (1-g)Q(.) that maximizes Pr (X). The algorithm is applicable to speech recognition. Let P(.) and Q(.) be two probability distributions over some alphabet A. Then Rg(.) = gP(.) + (1-g)Q(.),0

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Fast Algorithm for Computing the Optimal Linear Combination of Probability Distributions

The present invention relates to an algorithm which, given distributions P(.) and Q(.) and a sample output X, is directed to computing the optimal linear combination gP(.) + (1-g)Q(.) that maximizes Pr (X). The algorithm is applicable to speech recognition. Let P(.) and Q(.) be two probability distributions over some alphabet A. Then Rg(.) = gP(.) + (1-g)Q(.),0<g<1 is a linear combination of P and Q parameterized by the weighting value g. Let X = x1x2 ...xN be a sequence with xieA i=1,2,...N. Let

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be the probability of the sequence X being generated by Rg(.). Given P(.), Q(.) and X, the optimal value of g,i.e., the value of g that maximizes Prg (X), is sought. P(xi) is represented as Pi and Q(xi) as Qi . Then considering Prg (X) as a function if g for the given X,

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S(g) is a polynomial of degree N in terms of g. All of the roots of S(g) are real and occur outside the interval 0<g<1. Therefore, S(g) can have at most one local maximum in the interval 0<g<1. The derivative of S(g) is:

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Since S(g) is positive in the region 0<g<1, the sign of S'(g) is the same as the sign of T(g), where

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The present algorithm is based on evaluating the function T(g). Two cases are considered in which the maximum occurs at a boundary point g=0 or g=1. Case 1: S(g) is maximum at g=0 . <=> i) T(0) = 0 or ii) T(0) < 0 and T(1) < 0 Case 2: S(g...