1. A method for discriminating between speech and background regions, comprising: segmenting an input utterance into speech and background regions without knowledge of the lexical content of the input utterance to create a segmented input string; introducing insertion errors into the background regions that are error prone to generate error laden background strings; statistically modeling the segmented input string and the error laden background strings using a discriminative training algorithm to generate a model with adapted parameters; decoding the input utterance using the model with the adapted parameters; and outputting a recognized string based on the decoding step.
1. A method for discriminating between speech and background regions, comprising: segmenting an input utterance into speech and background regions without knowledge of the lexical content of the input utterance to create a segmented input string; introducing insertion errors into the background regions that are error prone to generate error laden background strings; statistically modeling the segmented input string and the error laden background strings using a discriminative training algorithm to generate a model with adapted parameters; decoding the input utterance using the model with the adapted parameters; and outputting a recognized string based on the decoding step. 5. The method as recited in claim 1, wherein the decoding step uses Viterbi decoding.