1. A method of context adaptation of a speech-to-speech translation system comprising the steps of: extracting a plurality of sets of paralinguistic attribute values from a plurality of input signals, wherein each set of the plurality of sets of paralinguistic attribute values is extracted from a corresponding input signal of the plurality of input signals via a corresponding classifier of a plurality of classifiers; generating a final set of paralinguistic attribute values for the plurality of input signals from the plurality of sets of paralinguistic attribute values; and modifying performance of at least one of a speech recognition module, a translation module and a text-to-speech module of the speech-to-speech translation system in accordance with the final set of paralinguistic attribute values for the plurality of input signals; wherein the set of paralinguistic attribute values that each classifier extracts is represented by a vector signal output by the classifier, the vector signal comprising two or more values corresponding to two or more paralinguistic attributes of interest such that the step of generating the final set of paralinguistic attribute values comprises combining each of the vector signals from each of the classifiers by combining values of common paralinguistic attributes of interest across the vector signals to yield a separate decision value for each of the two or more paralinguistic attributes of interest, the final set of paralinguistic attribute values comprising a plurality of decision values corresponding to respective ones of the two or more paralinguistic attributes of interest; further wherein the extracting, generating and modifying steps are implemented via instruction code that is executed by at least one processor device.
1. A method of context adaptation of a speech-to-speech translation system comprising the steps of: extracting a plurality of sets of paralinguistic attribute values from a plurality of input signals, wherein each set of the plurality of sets of paralinguistic attribute values is extracted from a corresponding input signal of the plurality of input signals via a corresponding classifier of a plurality of classifiers; generating a final set of paralinguistic attribute values for the plurality of input signals from the plurality of sets of paralinguistic attribute values; and modifying performance of at least one of a speech recognition module, a translation module and a text-to-speech module of the speech-to-speech translation system in accordance with the final set of paralinguistic attribute values for the plurality of input signals; wherein the set of paralinguistic attribute values that each classifier extracts is represented by a vector signal output by the classifier, the vector signal comprising two or more values corresponding to two or more paralinguistic attributes of interest such that the step of generating the final set of paralinguistic attribute values comprises combining each of the vector signals from each of the classifiers by combining values of common paralinguistic attributes of interest across the vector signals to yield a separate decision value for each of the two or more paralinguistic attributes of interest, the final set of paralinguistic attribute values comprising a plurality of decision values corresponding to respective ones of the two or more paralinguistic attributes of interest; further wherein the extracting, generating and modifying steps are implemented via instruction code that is executed by at least one processor device. 6. The method of claim 1 , wherein, in the step of combining values of common paralinguistic attributes, each set of paralinguistic attribute values comprise a plurality of confidence values, and each of the plurality of confidence values corresponds to a paralinguistic attribute value, and wherein the plurality of confidence values are utilized in combining values.