Spoken Handwriting Verification Using Statistical Models
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We are reporting on consolidated results obtained with a new user authentication system based on combined acquisition of online handwriting and speech signals. In our approach, signals are recorded by asking the user to say what she or he is simultaneously writing. This methodology has the clear advantage of acquiring two sources of biometric information at no extra cost in terms of time or inconvenience. We are proposing here two scenarios of use: spoken signature where the user signs and speaks at the same time and spoken handwriting where the user writes and says what is written. These two scenarios are implemented and fully evaluated using a verification system based on Gaussian Mixture Models (GMMs). The evaluation is performed on MyIdea, a realistic multimodal biometric database. Results show that the use of both speech and handwriting modalities outperforms significantly these modalities used alone, for both scenarios. Comparisons between the spoken signature and spoken handwriting scenarios are also drawn.