1. In a message recognition system, a method of transforming a consistent message into a message recognizable by the computer, the method comprising the steps of: (A) transforming the consistent message generated by a human in at least two formats into electrical signal representations of the consistent message; (B) producing from said electrical signal representations of the consistent message a set of parameters for each said format; (C) generating a likelihood score of recognition for each said set of parameters; (D) using said sets of parameters to train a weighting coefficient for each of the at least two formats of the consistent message, wherein said step of training a weighting coefficient comprises the steps of (i) partitioning the consistent message in each said format into one or more subunits, wherein each subunit corresponds to a piece of the consistent message, (ii) grouping said subunits from each said format into a plurality of groups, wherein each group comprises one said subunit from each said format, and wherein each said subunit in one said group corresponds to the same piece of the consistent message, (iii) determining a likelihood score of recognition for each said group of subunits, (iv) determining a global score for the consistent message based on said likelihood scores of recognition, and (v) using said global score to determine said weighting coefficients, (E) generating a weighted expression based on said trained weighting coefficient and said likelihood scores of recognition; and (F) selecting a candidate message unit that maximizes said weighted expression to transform said electrical signal representations of the consistent message into a computer recognizable message.
1. In a message recognition system, a method of transforming a consistent message into a message recognizable by the computer, the method comprising the steps of: (A) transforming the consistent message generated by a human in at least two formats into electrical signal representations of the consistent message; (B) producing from said electrical signal representations of the consistent message a set of parameters for each said format; (C) generating a likelihood score of recognition for each said set of parameters; (D) using said sets of parameters to train a weighting coefficient for each of the at least two formats of the consistent message, wherein said step of training a weighting coefficient comprises the steps of (i) partitioning the consistent message in each said format into one or more subunits, wherein each subunit corresponds to a piece of the consistent message, (ii) grouping said subunits from each said format into a plurality of groups, wherein each group comprises one said subunit from each said format, and wherein each said subunit in one said group corresponds to the same piece of the consistent message, (iii) determining a likelihood score of recognition for each said group of subunits, (iv) determining a global score for the consistent message based on said likelihood scores of recognition, and (v) using said global score to determine said weighting coefficients, (E) generating a weighted expression based on said trained weighting coefficient and said likelihood scores of recognition; and (F) selecting a candidate message unit that maximizes said weighted expression to transform said electrical signal representations of the consistent message into a computer recognizable message. 12. The method of claim 1, wherein said trained weighting coefficients are determined in said step (y) using a joint maximum likelihood procedure.