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. 13. The method as defined in claim 12 , wherein step (b) includes the step of performing a dynamic range utilization analysis on the sampled data to determine if the speech is at or below a preselected minimum threshold level indicating the speech was too quiet; and wherein the method further comprises the steps of: performing a clipping analysis on the sampled data to determine if the speech met or has exceeded a preselected maximum value indicating that clipping has occurred, providing an integrity score indicating quality of the speech, and requesting repeat of the speech responsive to the integrity score when the integrity score is below a preselected or selected value to thereby enhance speech-model development when in a training mode or to thereby enhance recognition accuracy when in a recognition mode.