11. A method for automatically recognizing speech, comprising: inputting a signal based on an utterance to be recognized, the signal having a plurality of portions; generating a proposed recognition for each portion; generating for each portion of the signal, a plurality of confidence measurements from the proposed recognition for that portion of the signal, comprising: generating at least one confidence measurement of a first type, and generating at least one confidence measurement of a second type, wherein at least the first and second types of confidence measurements are different types of confidence measurements that correspond to separate knowledge sources and the at least first and second types of confidence measurements are generated in parallel relative to the signal; normalizing, for each portion of the signal, the plurality of confidence measurements, comprising at least the first and second types of confidence measurements, for that portion of the signal; integrating, for each portion of the signal, the plurality of normalized confidence measurements of at least the first and second types to generate an integrated confidence measurement for that portion of the signal; and determining, for each portion of the signal, if the proposed recognition for that portion of the signal is acceptable based on the integrated confidence measurements of at least the first and second types.
11. A method for automatically recognizing speech, comprising: inputting a signal based on an utterance to be recognized, the signal having a plurality of portions; generating a proposed recognition for each portion; generating for each portion of the signal, a plurality of confidence measurements from the proposed recognition for that portion of the signal, comprising: generating at least one confidence measurement of a first type, and generating at least one confidence measurement of a second type, wherein at least the first and second types of confidence measurements are different types of confidence measurements that correspond to separate knowledge sources and the at least first and second types of confidence measurements are generated in parallel relative to the signal; normalizing, for each portion of the signal, the plurality of confidence measurements, comprising at least the first and second types of confidence measurements, for that portion of the signal; integrating, for each portion of the signal, the plurality of normalized confidence measurements of at least the first and second types to generate an integrated confidence measurement for that portion of the signal; and determining, for each portion of the signal, if the proposed recognition for that portion of the signal is acceptable based on the integrated confidence measurements of at least the first and second types. 18. The method of claim 11, wherein integrating the plurality of normalized confidence measurements comprises inputting the plurality of normalized confidence measurements into a multi-layer neural network.