6. The method of claim 1 , wherein the step of developing feature vectors utilizing the vocalization Doppler signals, comprises the steps of: for each wavelength utilized by the radar sensor in detecting the body motions of the speaker, converting the vocalization Doppler signals to an acoustic vector, articulator vector, and a speech energy vector; creating a separate feature vector for each wavelength based on a concatenation of a vector of information derived from the acoustic vector, articulator vector, and the speech energy vector; creating a joint feature vector based on a combination of the acoustic vectors, articulator vectors, and the speech energy vectors of each wavelength; and transmitting the feature vectors to the word classifier.
6. The method of claim 1 , wherein the step of developing feature vectors utilizing the vocalization Doppler signals, comprises the steps of: for each wavelength utilized by the radar sensor in detecting the body motions of the speaker, converting the vocalization Doppler signals to an acoustic vector, articulator vector, and a speech energy vector; creating a separate feature vector for each wavelength based on a concatenation of a vector of information derived from the acoustic vector, articulator vector, and the speech energy vector; creating a joint feature vector based on a combination of the acoustic vectors, articulator vectors, and the speech energy vectors of each wavelength; and transmitting the feature vectors to the word classifier. 8. The method of claim 6 , wherein the step of utilizing a word classifier for each feature vector to produce word decision data comprises the step of utilizing a Hidden Markov Model to produce the word decision.