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.
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. 20. The method as defined in claim 12 , wherein the step of estimating noise power includes the step of estimating noise power for each of a plurality of time slices on either side of the speech near and outside boundaries of the speech for each of a plurality of frequency ranges to thereby determine a background noise contour for noise within the observation window.