Abstract: A method and apparatus are provided for improving the performance of an interactive speech application. The interactive speech application is developed and deployed for use by one or more callers. During execution, the interactive speech application stores, in a log, event information that describes each task carried out by the interactive speech application in response to interaction with the one or more callers. The application also stores one or more sets of audio information, in which each of the sets of audio information is associated with one or more utterances by one of the callers. Each of the sets of audio information is associated with one of the tasks represented in the log. After the log is established, an analytical report is displayed. The report describes selective actions taken by the interactive speech application while executing, and selective actions taken by one or more callers while interacting with the interactive speech application.

Abstract: Improved system and method for speaker-independent speech token recognition are described. The system is neural network-based and involves processing a sequence of spoken utterances, e.g. separately articulated letters of a name, to identify the same based upon a highest probability match of each utterance with learned speech tokens, e.g. the letters of the English language alphabet, and based upon a highest probability match of the uttered sequence with a defined utterance library, e.g. a list of names. First, the spoken utterance is digitized or captured and processed into a spectral representation. Second, discrete time frames of the DFT are classified phonetically. Third, the time-frame outputs are used by a modified Viterbi search to locate segment boundaries, near which such segment boundaries lies the information that is needed to discriminate letters. Fourth, the segmented or bounded representation is reclassified using such information into individual hypothesized letters.