22 March 1999 Low-complexity speaker authentication techniques using polynomial classifiers
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Modern authentication systems require high-accuracy low complexity methods. High accuracy ensures secure access to sensitive data. Low computational requirements produce high transaction rates for large authentication populations. We propose a polynomial-based classification system that combines high-accuracy and low complexity using discriminative techniques. Traditionally polynomial classifiers have been difficult to use for authentication because of either low accuracy or problems associated with large training sets. We detail a new training method that solves these problems. The new method achieves high accuracy by implementing discriminative classification between in-class and out-of- class feature sets. A separable approach to the problem enables the method to be applied to large data sets. Storage is reduced by eliminating redundant correlations in the in- class and out-of-class sets. We also show several new techniques that can be applied to balance prior probabilities and facilitate low complexity retraining. We illustrate the method by applying it to the problem of speaker authentication using voice. We demonstrate the technique on a multisession speaker verification database collected over a one month period. Using a third order polynomial-based scheme, the new system gives less than one percent average equal error rate using only one minute of training data and less than five seconds of testing data per speaker.
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William M. Campbell, William M. Campbell, Charles C. Broun, Charles C. Broun, } "Low-complexity speaker authentication techniques using polynomial classifiers", Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999); doi: 10.1117/12.342890; https://doi.org/10.1117/12.342890

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