Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/172974
Title: Voice Activity Detection with >83% Accuracy under SNR down to -3dB at 1.19µW and 0.07mm2 in 40nm
Authors: TEO JINQ HORNG 
KARIM ALI ABDELTAWWAB AHMED 
ALIOTO,MASSIMO BRUNO 
Issue Date: 28-Jul-2020
Citation: TEO JINQ HORNG, KARIM ALI ABDELTAWWAB AHMED, ALIOTO,MASSIMO BRUNO (2020-07-28). Voice Activity Detection with >83% Accuracy under SNR down to -3dB at 1.19µW and 0.07mm2 in 40nm. IEEE ASSCC 2020. ScholarBank@NUS Repository.
Abstract: This work presents a voice activity detector for keyword spotting in self-powered speech interfaces with sub-syllable latency. A simple decision stump classifier and time averaging are introduced to provide >83% accuracy in noisy environments with SNR down to -3dB for reliable operation under a wide range of usage contexts (8-15dB lower than prior art). 1.19-µW power and 0.07mm2 area are shown in 40nm.
Source Title: IEEE ASSCC 2020
URI: https://scholarbank.nus.edu.sg/handle/10635/172974
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