1. A computer-implemented method of processing and classifying a communication involving at least one human, said method comprising: processing by the computer said communication to detect one or more composite words in a given context C, wherein each composite word is a predefined sequence of words S pre-programmed as an atomic unit to prevent partial detection; estimating a posterior probability P(S|C,X), wherein X is an acoustics vector sequence and wherein said posterior probability P(S|C,X)=P(X|C,S)P(S)/P(X|C,S)+P(X|C,S)), wherein S is an anti model of S; determining an overall confidence level of events expressed in said one or more composite words occurring in said communication based on said posterior probability; and classifying said communication into one or more categories based on detected events in accordance with said overall confidence level.
1. A computer-implemented method of processing and classifying a communication involving at least one human, said method comprising: processing by the computer said communication to detect one or more composite words in a given context C, wherein each composite word is a predefined sequence of words S pre-programmed as an atomic unit to prevent partial detection; estimating a posterior probability P(S|C,X), wherein X is an acoustics vector sequence and wherein said posterior probability P(S|C,X)=P(X|C,S)P(S)/P(X|C,S)+P(X|C,S)), wherein S is an anti model of S; determining an overall confidence level of events expressed in said one or more composite words occurring in said communication based on said posterior probability; and classifying said communication into one or more categories based on detected events in accordance with said overall confidence level. 7. The method of claim 1 , in which said events are linguistic events.