A cochlear neuron based robust feature for speaker recognition

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

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Datao You, Tao Jiang, Jiqing Han, Tieran Zheng
• Keywords: Cochlear Neurons, ICASSP 2011, Robust Feature, Sensitive Cochlear Neurons, Signal Processing