27. A method to provide speech-activated control of aerial vehicle components, the method comprising the steps of: (a) performing noise reduction processing on sampled speech data representing a command annunciation to remove noise from within and around speech formants of the sampled speech, 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 speech 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 speech data; (b) developing a set of feature vectors representing energy of a frequency content of the sampled speech data to thereby determine a unique pattern identifying the command annunciation; (c) applying a speech recognition engine to the set of feature vectors to thereby form at least one speech recognition model; (d) associating an index with the at least one speech recognition model associated with the command annunciation; and (e) storing the at least one speech recognition model and the assigned index.
27. A method to provide speech-activated control of aerial vehicle components, the method comprising the steps of: (a) performing noise reduction processing on sampled speech data representing a command annunciation to remove noise from within and around speech formants of the sampled speech, 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 speech 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 speech data; (b) developing a set of feature vectors representing energy of a frequency content of the sampled speech data to thereby determine a unique pattern identifying the command annunciation; (c) applying a speech recognition engine to the set of feature vectors to thereby form at least one speech recognition model; (d) associating an index with the at least one speech recognition model associated with the command annunciation; and (e) storing the at least one speech recognition model and the assigned index. 31. The method as defined in claim 27 , wherein the sampled speech data is a first sampled data processed to form a stored speech model during a speech recognition model training event, wherein the command annunciation is a first command annunciation, and wherein the method further comprises the steps of: responsive to a second command annunciation received during a recognition event, performing a dynamic range utilization analysis on a second sampled data to determine if the speech is below a preselected minimum threshold level indicating the speech was too quiet; performing a clipping analysis on the second sampled data to determine if the speech has exceeded a preselected maximum value indicating that clipping has occurred; and performing a cropping analysis on the second sampled data to determine if the speech is potentially missing portions of energy.