No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Method of adding vocabulary to a speech recognition system
Abstract
A method and a system for use in a computer speech recognition system for adding new vocabulary by using language model statistics corresponding to an existing vocabulary word. The method involves a series of steps including receiving a user input identifying a first word for which no language model statistics exist in the speech recognition system. The first word is for inclusion within the existing vocabulary of the speech recognition system. In response to a second user input identifying a second word for which language model statistics exist in the speech recognition system, recalling from a computer memory the language model statistics for the second word. The speech recognition system then automatically creates language model statistics for the first word by duplicating the language model statistics of the second word and replacing each occurrence of the second word in the duplicated language model statistics with the first word.
ResearchGate has not been able to resolve any citations for this publication.
A method determines if a portion of speech corresponds to a speech pattern by time aligning both the speech and a plurality of speech pattern models against a common time-aligning model. This compensates for speech variation between the speech and the pattern models. The method then compares the resulting time-aligned speech model against the resulting time-aligned pattern models to determine which of the patterns most probably corresponds to the speech. Preferably there are a plurality of time-aligning models, each representing a group of somewhat similar sound sequences which occur in different words. Each of these time-aligning models is scored for similarity against a portion of speech, and the time-aligned speech model and time-aligned pattern models produced by time alignment with the best scoring time-aligning model are compared to determine the likelihood that each speech pattern corresponds to the portion of speech. This is performed for each successive portion of speech. When a portion of speech appears to correspond to a given speech pattern model, a range of likely start times is calculated for the vocabulary word associated with that model, and a word score is calculated to indicate the likelihood of that word starting in that range. The method uses a more computationally intensive comparison between the speech and selected vocabulary words, so as to more accurately determine which words correspond with which portions of the speech. When this more intensive comparison indicates the ending of a word at a given point in the speech, the method selects the best scoring vocabulary words whose range of start times overlaps that ending time, and performs the computationally intensive comparison on those selected words starting at that point in the speech.