Abstract: A speech application is analyzed to automatically identify timing problems therein. Information related to the application is recorded and problems are identified. A recognizer can be utilized to perform recognition on the recorded information.

Abstract: An expected dialog-turn (ED) value is estimated for evaluating a speech application. Parameters such as a confidence threshold setting can be adjusted based on the expected dialog-turn value. In a particular example, recognition results and corresponding confidence scores are used to estimate the expected dialog-turn value. The recognition results can be associated with a possible outcome for the speech application and a cost for the possible outcome can be used to estimate the expected dialog-turn value.

Abstract: An acoustic model includes word-specific models, that are specific to candidate words. The candidate words would otherwise be mapped to a series of general phones. A sub-series of the general phones representing the candidate word is modeled by a new phone and the new phone is dedicated to the candidate word, or a small group of similar words, but the new phone is not shared among all words that otherwise map to the sub-series of general phones.

Abstract: The method and apparatus utilize a filter to remove a variety of non-dictated words from data based on probability and improve the effectiveness of creating a language model.

Abstract: The present invention relates to a method of processing speech, in which input speech is processed to determine an input speech vector (or) representing a sample of the speech. A number of possible output states are defined with each output state (j) being represented by a number of state mixture components (m). Each state mixture component is then approximated by a weighted sum of a number of predetermined generic components (x), allowing the likelihoods of each output states (j) corresponding to the input speech vector (or) to be determined.