Speaker verification and identification using gamma neural networks

Gamma neural networks are used for speaker verification and identification in this paper. When input features are cepstral coefficients, the memory depth of the gamma networks can be adjusted on-line and the outputs of the gamma memory may be viewed as a combination of cepstral and delta cepstral coefficients with adaptable weights. So, gamma networks are very suitable to grasp the dynamics of speech. Simulation results show that the gamma networks outperform other neural approaches for speaker identification and verification in TIMIT database experiments.