A novel neural feature for a text-dependent speaker identification system
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Abstract
A novel feature based on the simulated neural response of the auditory periphery was proposed in this study for a speaker identification system. A well-known computational model of the auditory-nerve (AN) fiber by Zilany and colleagues, which incorporates most of the stages and the relevant nonlinearities observed in the peripheral auditory system, was employed to simulate neural responses to speech signals from different speakers. Neurograms were constructed from responses of inner-hair-cell (IHC)-AN synapses with characteristic frequencies spanning the dynamic range of hearing. The synapse responses were subjected to an analytical function to incorporate the effects of absolute and relative refractory periods. The proposed IHC-AN neurogram feature was then used to train and test the text-dependent speaker identification system using standard classifiers. The performance of the proposed method was compared to the results from existing baseline methods for both quiet and noisy conditions. While the performance using the proposed feature was comparable to the results of existing methods in quiet environments, the neural feature exhibited a substantially better classification accuracy in noisy conditions, especially with white Gaussian and street noises. Also, the performance of the proposed system was relatively independent of various types of distortions in the acoustic signals and classifiers. The proposed feature can be employed to design a robust speech recognition system.
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