DocumentCode :
2179756
Title :
A cochlear neuron based robust feature for speaker recognition
Author :
You, Datao ; Jiang, Tao ; Han, Jiqing ; Zheng, Tieran
Author_Institution :
Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., Harbin, China
fYear :
2011
fDate :
22-27 May 2011
Firstpage :
5440
Lastpage :
5443
Abstract :
In this paper, a robust feature for text-independent speaker recognition is proposed, which simulate the response mode of cochlear neurons in processing acoustic signal. The feature is derived from sparse coding coefficient which is computed on a learned over-complete dictionary, and the dictionary is considered similar to part of speech sensitive cochlear neurons. Furthermore, the feature is generated without dimension reducing and de-correlation. The robust feature is implemented to address the problem of mismatch situation between training and testing. Experiments show that the proposed feature outperforms the Mel-frequency cepstral coefficients (MFCC) feature, especially under noisy environments, the equal error rate (EER) of the MFCC drops to 21.6% (10 dB) from 10.3% (25 dB), while the EER of the proposed feature is also 6.6% (10 dB) with no degradation.
Keywords :
acoustic signal processing; speaker recognition; EER; MFCC feature; Mel-frequency cepstral coefficient feature; acoustic signal processing; cochlear neuron; equal error rate; sparse coding coefficient; text-independent speaker recognition; Databases; Dictionaries; Encoding; Mel frequency cepstral coefficient; Neurons; Robustness; Speaker recognition; Robust feature extraction; auditory; cochlear neurons; sparse coding; speaker recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Conference_Location :
Prague
ISSN :
1520-6149
Print_ISBN :
978-1-4577-0538-0
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2011.5947589
Filename :
5947589
Link To Document :
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