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
Multi-speaker voice cryptographic key generation
AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
Cryptographic-speech-key generation architecture improvements
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
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In this paper an improvement in the generation of the crypto-graphic-speech-key by optimising the number of parameters is presented. It involves the selection of the number of dimensions with the best performance for each of the phonemes. First, the Mel frequency cepstral coefficients, (first and second derivatives) of the speech signal are calculated. Then, an Automatic Speech Recogniser, which models are previously trained, is used to detect the phoneme limits in the speech utterance. Afterwards, the feature vectors are built using both the phoneme-speech models and the information obtained from the phoneme segmentation. Finally, the Support Vector Machines classifier, relying on an RBF kernel, computes the cryptographic key. By optimising the number of parameters our results show an improvement of 19.88%, 17.08%, 14.91% for 10, 20 and 30 speakers respectively, employing the YOHO database.