Hi all, I know this is quite a specialised topic but I was just wondering if any of the members might have a bit of advise for me if they have worked within the speech field before. I am writing a speaker identification system within C++. The feature I am using from the voiced data is the glottal period, which is unique to each speaker. The feature extraction module works greater, however I think I need to use some sort of stochastic model to create a probability as to whether speaker x, is speaker x based upon their live sample compared against all the others within the database. I was wondering whether I should use Hidden Markov Models for this, or distance templates. Each template that is produced from the extraction module contains the length of the glottal cycle in MS, and the time at which it occurred during enrolment. Any help on this would be appreciated… thanks. BTW, the system will be operating within Text-Dependant mode.