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Real-time speaker identification using the AEREAR2 event-based silicon cochlea


Li, C H; Delbruck, T; Liu, S C (2012). Real-time speaker identification using the AEREAR2 event-based silicon cochlea. In: IEEE International Symposium on Circuits and Systems (ISCAS) 2012, Seoul, South Korea, 20 May 2012 - 23 May 2012, 1159-1162.

Abstract

This paper reports a study on methods for real-time speaker identification using the output from an event-based silicon cochlea. These methods are evaluated based on the amount of computation that needs to be performed and the classification performance in a speaker identification task. It uses the binaural AEREAR2 silicon cochlea, with 64 frequency channels and 512 output neurons. Auditory features representing fading histograms of inter-spike intervals and channel activity distributions are extracted from the cochlea spikes. These feature vectors are then classified by a linear Support Vector Machine, which is trained against a subset of 40 speakers (20/20 male/female) from the TIMIT database. Speakers are correctly identified at >90% accuracy during each sentence utterance and with an average latency of 700±200ms from the start of the sentence.

Abstract

This paper reports a study on methods for real-time speaker identification using the output from an event-based silicon cochlea. These methods are evaluated based on the amount of computation that needs to be performed and the classification performance in a speaker identification task. It uses the binaural AEREAR2 silicon cochlea, with 64 frequency channels and 512 output neurons. Auditory features representing fading histograms of inter-spike intervals and channel activity distributions are extracted from the cochlea spikes. These feature vectors are then classified by a linear Support Vector Machine, which is trained against a subset of 40 speakers (20/20 male/female) from the TIMIT database. Speakers are correctly identified at >90% accuracy during each sentence utterance and with an average latency of 700±200ms from the start of the sentence.

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Additional indexing

Item Type:Conference or Workshop Item (Speech), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:23 May 2012
Deposited On:28 Feb 2013 07:45
Last Modified:07 Dec 2017 20:20
Series Name:IEEE International Symposium on Circuits and Systems. Proceedings
Number of Pages:1
ISSN:0271-4302
ISBN:978-1-4673-0218-0
Additional Information:© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher DOI:https://doi.org/10.1109/ISCAS.2012.6271438

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