Robust speaker identification using an auditory-based feature

An auditory-based feature extraction algorithm is presented. The feature is based on a recently published time-frequency transform plus a set of modules to simulate the signal processing functions in the cochlea. The feature is applied to a speaker identi cation task to address the acoustic mismatch problem between training and testing. Usually, the performances of acoustic models trained in clean speech drop signi cantly when tested on noisy speech. The proposed feature has shown strong robustness in the mismatched situation. As shown in our experiments, in a speaker identi cation task, both MFCC and the proposed feature have near perfect performances in a clean testing condition, but when the SNR of input signal drops to 6

• Type: Conference
• Year: 2010
• Where: ICASSP
• Authors: Qi Li, Yan Huang
• Keywords: Acoustic Mismatch Problem, Cation Task, Feature Extraction Algorithm, ICASSP 2010, Signal Processing