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Abstract

A tutorial instructs how to use a word recognition system, such as one for speech recognition. It specifies a set of allowed response words for each of a plurality of states. It sends messages on how to use the recognizer in certain states, and, in others, presents exercises in which the user is to enter signals representing expected words. It scores each such signal against word models to select which response word corresponds to it, and then advances to a state associated with that selected response. This scoring is performed against a large vocabulary even though only a small number of responses are allowed, and the signal is rejected if too many non-allowed words score better than any allowed word. The system comes with multiple sets of standard signal models; it scores each against a given user's signals, selects the set which scores best, and then performs adaptive and batch training upon that set. Preferably, the tutorial prompts users to enter the words used for training in an environment similar to that of the actual recognizer the tutorial is training them to use. The system will normally simulate the recognition of the prompted word, but will sometimes it will simulate an error. When it does, notifies the user if he fails to correct the error. The recognizer associated with the tutorial allows users to perform adaptive training either on all words, or only on those whose recognition has been corrected or confirmed. The recognizer also uses a context language model which indicates the probability that a given word will be used in the context of other words which precede it in a grouping of text.

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