Speaker identification with dynamic synapses
Alireza Afshordi Dibazar, Hassan H. Namarvar, Jim‐Shih Liaw, Theodore W. Berger · The Journal of the Acoustical Society of America · 2001
In this paper, we have proposed a new method of presenting a speech signal to the dynamic synapses neural network (DSNN) and optimal selection of input neurons for the speaker identification task. The DSNN developed by Liaw and Berger (1996) was employed to capture the speaker information from the action potentials, which was generated by wavelet filter banks. In order to sufficiently capture the speaker information, the time-delayed temporal patterns of action potentials are used as the inputs of the network. The optimal number of input neurons obtained by minimizing the total output error of the network while identifying a target speaker and rejecting the other unknown speakers. The genetic algorithm was employed to design a supervised learning rule for training of the network. The TI-46 dataset was used in both the training and testing phase. Comparative results with previous methods showed a better performance of the correct classification for the model-based dynamic synapses speaker identification system. [Work supported by DARPA and ONR.]