Texas Instruments Corporation

Jayant M Naik

George R Doddington

Lorin Paul Netsch

Speaker verification is performed by computing principal components of a fixed text statement comprising a speaker identification code and a two-word phrase, and principal spectral components of a random word phrase. A multi-phrase strategy is utilized in access control to allow successive verification attempts in a single session, if the speaker fails initial attempts. Based upon a verification attempt, the system produces a verification score which is compared with a threshold value. On successive attempts, the criterion for acceptance is changed, and one of a number of criteria must be satisfied for acceptance in subsequent attempts. A speaker normalization function can also be invoked to modify the verification score of persons enrolled with the system who inherently produce scores which result in denial of access. Accuracy of the verification system is enhanced by updating the reference template which then more accurately symbolizes the person's speech signature.

Fixed text speaker verification method and apparatus

An apparatus and method for verifying the identity of an unknown user of a telephone calling card service is disclosed which receives identification information corresponding to a valid user of the service, requests the unknown user to speak an authorization phrase without disclosing the authorization phrase to the unknown user, receives the unknown user's speech signal in response to the request, and verifies the identity of the unknown user based on the received speech signal. Other apparatus, systems, and methods are also disclosed.

Voice verification circuit for validating the identity of telephone

A speaker verification system receives input speech from a speaker of unknown identity. The speech undergoes linear predictive coding (LPC) analysis and transformation to maximize separability between true speakers and impostors when compared to reference speech parameters which have been similarly transformed. The transformation incorporated a 'inter-class' covariance matrix of successful impostors within a database.

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Dallas, TX, United States of America

Jayant M Naik