METHOD FOR VOICE RECOGNITION DEVICE

    公开(公告)号:JP2000242293A

    公开(公告)日:2000-09-08

    申请号:JP2000036105

    申请日:2000-02-15

    Applicant: MOTOROLA INC

    Abstract: PROBLEM TO BE SOLVED: To make storable trace-back information needed for HMM tracing by using a smaller memory by dividing the signal representation of a pronounced voice into frames, allocating the respective frames to respective states by using adjusting algorithm, and discriminating state transition to respective states. SOLUTION: A sound signal received from a microphone 114 is converted by an analog-digital converter 202 into a digital signal. A speech call processor inplements a feature extracting function 204 for the processed digital signal representation of the analog signal output of the microphone 114 to generate one group of feature vectors representing the pronounced voice of the user. And, one feature vector is present for every frame. The processor performs speech recognition 206 or training 207 by making use of the feature vectors. In training mode, the feature vector of the pronounced voice is used to generate a template in HMM format to be stored in a memory 208.

    Method of evaluating an utterance in a speech recognition system

    公开(公告)号:GB2333877B

    公开(公告)日:2001-08-08

    申请号:GB9900679

    申请日:1999-01-14

    Applicant: MOTOROLA INC

    Abstract: The present invention provides a method of calculating, within the framework of a speaker dependent system, a standard filler, or garbage model, for the detection of out-of-vocabulary utterances. In particular, the method receives new training data in a speech recognition system (202); calculates statistical parameters for the new training data (204); calculates global statistical parameters based upon the statistical parameters for the new training data (206); and updates a garbage model based upon the global statistical parameters (208). This is carried out on-line while the user is enrolling the vocabulary. The garbage model described in this disclosure is preferably an average speaker model, representative of all the speech data enrolled by the user to date. Also, the garbage model is preferably obtained as a by-product of the vocabulary enrollment procedure and is similar in it characteristics and topology to all the other regular vocabulary HMMs.

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