2. The method of claim 1 wherein clustering comprises: clustering the sets of acoustic information together if the corresponding speech recognition results have a similar semantic analysis.
1. A computer implemented method of processing acoustic information, comprising: extracting data representing a plurality of sets of acoustic information of interest from a data store; performing speech recognition using a computer with an application grammar and a second grammar on the data to obtain speech recognition results; identifying whether the speech recognition results are generated with the application grammar or the second grammar; performing a semantic analysis of the second data; clustering the sets of acoustic information into clusters based on the semantic analysis of the speech recognition results; ranking the clusters based on a number of instances of utterances contained in each cluster; removing clusters based on a consistency threshold calculation of each cluster that indicates when a cluster has a number of unlike utterances that have meaningful semantics; identifying, for each cluster, and storing an indicator of a set of acoustic information as being representative of a corresponding cluster, wherein the identified set of acoustic information is selected from the sets of acoustic information in each corresponding cluster; identifying the representative set of acoustic information for a given cluster as representing either a semantic intent covered by the application or as an unrepresented semantic intent based on the speech recognition results assigned to the given cluster were generated using the application grammar or the second grammar; and generating a revision to the application grammar to accommodate for the unrepresented semantic intent.