Abstract: Architecture for testing an application grammar for the presence of confusable terms. A grammar confusability metric (GCM) is generated for describing a likelihood that a reference term will be confused by the speech recognizer with another term phrase currently allowed by active grammar rules. The GCM is used to flag processing of two phrases in the grammar that have different semantic meaning, but that the speech recognizer could have difficulty distinguishing reliably. A built-in acoustic model is analyzed and feature vectors generated that are close to the acoustic properties of the input term. The feature vectors are then sent for recognition. A statistically random sampling method is applied to explore the acoustic properties of feature vectors of the input term phrase spatially and temporally. The feature vectors are perturbed in the neighborhood of the time domain and the Gaussian mixture model to which the feature vectors belong.