2. The method according to claim 1 , further comprising building the stored multiple recognition models by: recording utterances from a plurality of persons, wherein at least some of the recorded utterances are made with background noise audible to each person but not included in the recording of the utterances, and wherein the background noise is varied with different recordings to create a Lombard corpus; and classifying the recorded utterances in response to the Lombard characteristic to create a plurality of classifications.
1. A speech recognition method, comprising: storing multiple recognition models for a vocabulary set, each model classified according to a Lombard cluster, wherein each model is distinguishable from the other models based on a Lombard characteristic, the Lombard characteristic representing changes in an utterance pitch and volume in the presence of background noise; detecting at least one speaker utterance in a motor vehicle; selecting, via a processing device, one of the multiple recognition models in response to a Lombard characteristic of the at least one speaker utterance; utilizing the selected recognition model to recognize the at least one speaker utterance; and providing a signal in response to the recognition.