Speech recognition system, training arrangement and method of calculating iteration values for free parameters of a maximum-entropy speech model

A method of calculating iteration values for free parameters λα in the maximum-entropy speech model in accordance with the following general training algorithm:

• λα(n+1)|αεAi(n)=G(λα(n),mα,mα(n), . . . )|αεAi(n)
• where:
• n:
• refers to an iteration parameter that represents a current iteration step;
• Ai:
• represents the i-th attributes group in the speech model, where 1≦i≦m;
• Ai(n):
• represents the attributes group selected in the n-th iteration step;
• α:
• represents a attribute in the speech model;
• G:
• represents a mathematical function;
• λα(n):
• represents the n-th iteration value for the free parameter λα;
• mα:
• represents a desired boundary value for the attribute α;
• and
• mα(n):
• represents the n-th iteration boundary value for the desired boundary value mα, where one attribute group Ai(n) from a total of m speech model attribute groups is assigned to each iteration parameter n, and where the iteration values λα(n+1) are calculated for each and every attribute α from the currently assigned attribute group Ai(n), characterized in that the current iteration parameter n is assigned the attribute group Ai(n), where 1≦i(n)≦m, for which, in accordance with a predefined criterion, the adaptation of the iteration boundary values mα(n) to the respective associated desired boundary values mα is the worst of all the m attribute groups of the speech model.