Abstract
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. © 2007 IEEE.
Recommended Citation
I. Uysal et al., "Spike-based Feature Extraction For Noise Robust Speech Recognition Using Phase Synchrony Coding," Proceedings IEEE International Symposium on Circuits and Systems, pp. 1529 - 1532, article no. 4252942, Institute of Electrical and Electronics Engineers, Jan 2007.
The definitive version is available at https://doi.org/10.1109/iscas.2007.378702
Department(s)
Electrical and Computer Engineering
International Standard Serial Number (ISSN)
0271-4310
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2007
