Learning self-organized topology-preserving complex speech features at primary auditory cortex

Authors:
Taesu Kim;Soo-Young Lee
Affiliations:
Departments of BioSystems & EECS, Brain Science Research Center, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong gu, Daejeon 305 701, Republic of Korea;Departments of BioSystems & EECS, Brain Science Research Center, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong gu, Daejeon 305 701, Republic of Korea
Venue:
Neurocomputing
Year:
2005

Citing 2
Cited 2

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Auditory sparse representation for robust speaker recognition based on tensor structure

EURASIP Journal on Audio, Speech, and Music Processing - Intelligent Audio, Speech, and Music Processing Applications

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Abstract

By applying independent component analysis (ICA) algorithm to auditory signals a computational model was developed for the speech feature extraction at the primary auditory cortex. Unlike the other ICA-based features with simple frequency selectivity at the basilar membrane and inner hair cells the learnt features represent complex signal characteristics at the primary auditory cortex such as onset/offset and frequency modulation in time. Also, the topology is preserved with the help of neighborhood coupling during the self-organization. The extracted complex features demonstrated good performance for the robust discrimination of speech phonemes.