Cryptographic-speech-key generation architecture improvements

In this work we show a performance improvement of our system by taking into account the weights of the mixture of Gaussians of the Hidden Markov Model. Furthermore and independently tunning of each of the phoneme Support Vector Machine (SVM) parameters is performed. In our system the user utters a pass phrase and the phoneme waveform segments are found using the Automatic Speech Recognition Technology. Given the speech model and the phoneme information in the segments, a set of features are created to train an SVM that could generate a cryptographic key. Applying our method to a set of 10, 20, and 30 speakers from the YOHO database, the results show a good improvement compared with our last configuration, improving the robustness in the generation of the cryptographic key.