12. A method to provide speech-activated control of aerial vehicle components, the method comprising the steps of: (a) sampling a speech signal representing speech to define sampled data; (b) performing an integrity check on the sampled data to identify when the speech is below a preselected standard; (c) aligning the sampled data in an observation window to enhance analysis of the speech; (d) performing noise reduction processing to remove noise from within and around speech formants, to include: estimating noise power for each separate set of a plurality of bins having a same frequency range across a plurality of time slices of the sampled data to thereby provide a plurality of frequency range-specific noise power estimates, equalizing energy values of each set of bins having a same frequency range across the plurality of time slices responsive to the respective frequency range-specific noise power estimate, and thresholding each equalized bin by a parameterized threshold value to remove noise from within and around speech formants of the sampled data; (e) developing a set of feature vectors representing energy of a frequency content of the sampled data to thereby determine a unique pattern; and (f) applying a speech recognition engine to the set of feature vectors to perform one of the following functions: forming at least one speech recognition model associated with the speech signal, and matching the speech signal to the at least one speech recognition model.
16. The method as defined in claim 12 , wherein step (e) includes developing a first set of feature vectors for Hidden Markov Model (HMM) modeling and a second set of feature vectors for Neural Network (NN) modeling; and wherein step (f) includes applying a HMM speech recognition engine to the first set of feature vectors and a NN speech recognition engine to the second set of feature vectors; and wherein the method further comprises executing a voting scheme between a plurality of speech recognition engines including at least one of the following: the HMM speech recognition engine and the NN speech recognition engine.