A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Fundamentals of speech recognition
Fundamentals of speech recognition
Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Cryptographic Key Generation from Voice
SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Features and measures for speaker recognition
Features and measures for speaker recognition
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In this research we propose a new scheme for generating binary vectors, which can be used as keys for cryptographic purposes. These vectors are obtained from the speech signal and from the spoken user passphrase. The key bits are built using the Automatic Speech Recognition Technology to detect the phoneme limits in the speech utterance and the Support Vector Machines technique for classification. Linear prediction cepstral coefficients, (first and second derivatives) of the speech signal are calculated to create a 39-dimensional hyperspace. Then a hyperplane is created using an RBF kernel, and the SVM classifies the user's phonemes. Applying our method to a set of 10, 20, 30 and 50 speakers from the YOHO database, the results show that this method is sufficiently robust to reliably regenerate the cryptographic key.