@inproceedings{3734dc3aba8d4c7bacdfc520431f6da8,
title = "A cochlear neuron based robust feature for speaker recognition",
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 = "Robust feature extraction, auditory, cochlear neurons, sparse coding, speaker recognition",
author = "Datao You and Tao Jiang and Jiqing Han and Tieran Zheng",
year = "2011",
doi = "10.1109/ICASSP.2011.5947589",
language = "英语",
isbn = "9781457705397",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "5440--5443",
booktitle = "2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings",
note = "36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 ; Conference date: 22-05-2011 Through 27-05-2011",
}