Spike-Based Feature Extraction for Noise Robust Speech Recognition Using Phase Synchrony Coding

We propose a noise robust feature extraction technique for speech signals using phase synchrony. The front-end employs a psychoacoustic cochlea model with inner hair cells to transform speech into a parallel stream of spike trains as observed in the auditory nerve fibers. The degree of phase synchrony among nerve fibers with similar characteristic frequencies is calculated to yield a feature vector which shows little degradation in response to increasing levels of noise. As a benchmark, the feature set is used in a biologically plausible model with a spike-based, liquid state machine classifier for a simple acoustic classification task. Though applied to a simplified domain, the results indicate a superior performance when compared to a conventional speech recognition system, especially at very low signal-to-noise ratios.