Itt Defense, Inc.

Itt Corporation

William Y Huang

Lawrence G Bahler

Alan L Higgins

Joe A Naylor

A system and a method is disclosed for verifying a voice of a user conducting a telephone transaction. The system and method includes a mechanism for prompting the user to speak in a limited vocabulary. A feature extractor converts the limited vocabulary into a plurality of speech frames. A pre-processor is coupled to the feature extractor for processing the plurality of speech frames to produce a plurality of processed frames. The processing includes frame selection, which eliminates each of the plurality of speech frames having an absence of words. A Viterbi decoder is also coupled to said feature extractor for assigning a frame label to each of the plurality of speech frames to produce a plurality of frame labels. The processed frames and frame labels are then combined to produce a voice model, which includes each of the plurality of frame labels that correspond to the number of plurality of processed frames. A mechanism is also provided for comparing the voice model with the claimant's voice model, derived during a previous enrollment session. The voice model also is compared with an alternate voice model set, derived during previous enrollment sessions. The identity claimed is accepted if the voice model matches the claimant's voice model better than the alternative voice model set.

System for voice verification using matched frames

System for voice verification of telephone transactions

A method for recognizing spoken utterances of a speaker is disclosed, the method comprising the steps of providing a database of labeled speech data; providing a prototype of a Hidden Markov Model (HMM) definition to define the characteristics of the HMM; and parameterizing speech utterances according to one of linear prediction parameters or Mel-scale filter bank parameters. The method further includes selecting a frame period for accommodating the parameters and generating HMMs and decoding to specified speech utterances by causing the user to utter predefined training speech utterances for each HMM. The method then statistically computes the generated HMMs with the prototype HMM to provide a set of fully trained HMMs for each utterance indicative of the speaker. The trained HMMs are used for recognizing a speaker by computing Laplacian distances via distance table lookup for utterances of the speaker during the selected frame period; and iteratively decoding node transitions corresponding to the spoken utterances during the selected frame period to determine which predefined utterance is present.

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San Diego, CA, United States of America

William Y Huang