15. A method of speech recognition comprising the steps of: accessing a plurality of weak classifiers, wherein each weak classifier comprises a decision function for determining the presence of an event within a training corpus of known-class digitized speech utterances, and an ensemble detector comprising a plurality of the weak classifiers, that together are better at determining the presence of a speech signal event than any of the constituent weak classifiers; receiving a speech signal; digitizing the received speech signal; detecting locations of relevant speech signal events in the received and digitized speech signal, wherein each of the relevant speech signal events comprise spectral information and temporal information; capturing spectral features of and temporal relationships between all of the speech signal events; segmenting the received and digitized speech signal based on the detected locations of the speech signal events; analyzing the segmented, received and digitized speech signal, wherein the analysis is synchronized with the speech signal events, detecting patterns in the digitized speech signal with the captured spectral information, the temporal relationships, and the analyzed speech signal; recognizing speech data that corresponds with the analyzed digitized speech signal, wherein the step of recognizing the speech data comprises the steps of providing a list of perceptual alternatives for the recognized speech data that corresponds to the detected patterns in the digitized speech signal, and disambiguating between the perceptual alternatives for the recognized speech data based on the analysis of one or more of the speech signal events to improve the recognized speech data; and outputting the improved speech data.
15. A method of speech recognition comprising the steps of: accessing a plurality of weak classifiers, wherein each weak classifier comprises a decision function for determining the presence of an event within a training corpus of known-class digitized speech utterances, and an ensemble detector comprising a plurality of the weak classifiers, that together are better at determining the presence of a speech signal event than any of the constituent weak classifiers; receiving a speech signal; digitizing the received speech signal; detecting locations of relevant speech signal events in the received and digitized speech signal, wherein each of the relevant speech signal events comprise spectral information and temporal information; capturing spectral features of and temporal relationships between all of the speech signal events; segmenting the received and digitized speech signal based on the detected locations of the speech signal events; analyzing the segmented, received and digitized speech signal, wherein the analysis is synchronized with the speech signal events, detecting patterns in the digitized speech signal with the captured spectral information, the temporal relationships, and the analyzed speech signal; recognizing speech data that corresponds with the analyzed digitized speech signal, wherein the step of recognizing the speech data comprises the steps of providing a list of perceptual alternatives for the recognized speech data that corresponds to the detected patterns in the digitized speech signal, and disambiguating between the perceptual alternatives for the recognized speech data based on the analysis of one or more of the speech signal events to improve the recognized speech data; and outputting the improved speech data. 18. The method of claim 15 , wherein the step of accessing a sample of the plurality of speech signals having matching event patterns further comprises the step of automatically identifying regions in the plurality of speech signals that contain the event patterns, which comprises the steps of: accessing a training set; converting the speech signal into time-trajectory space regions containing all of the speech signal events from positive training examples; and repeatedly performing the steps of computing the counts of negative examples for all of the time-traiectory space regions, selecting a region of the time-trajectory space regions with the fewest events from negative training examples, and eliminating negative examples with no speech signal events in the selected region from further consideration; until a cascade is created that operates perfectly on said training set.