Phoneme classification using the auditory neurogram

In order to mimic the capability of human listeners identifying speech in noisy environments, this paper proposes a phoneme classification technique using simulated neural responses from a physiologically based computational model of the auditory periphery instead of using features directly from the acoustic signal. The 2-D neurograms were constructed from the simulated responses of the auditory-nerve fibers to speech phonemes. The features of the neurograms were extracted using the Radon transform and used to train the classification system using a deep neural network classifier. Classification performance was evaluated in quiet and under noisy conditions for different types of phonemes extracted from the TIMIT database. Based on simulation results, the proposed method outperformed most of the traditional acoustic-property-based phoneme classification methods for both in quiet and under noisy conditions. The proposed method could easily be extended to develop an automatic speech recognition system.