Abstract
It is well known that MFCC based speaker identification (SID) systems easily break down under mismatched training and test conditions. One such mismatch occurs when a SID system is trained on anechoic speech data, while test is carried out using reverberant data collected via a distant microphone. In this study, a new set of feature parameters based on the Hilbert envelope of Gammatone filterbank outputs is proposed to improve SID performance in the presence of room reverberation. Considering two distinct perceptual effects of reverberation on speech signals, i.e., coloration and long-term reverberation, two different compensation strategies are integrated within the feature extraction framework to effectively suppress the effects of reverberation. Experimental evaluation is performed using speech material from the TIMIT, four different measured room impulse responses (RIR) from Aachen impulse response (AIR) database, and a GMM-based SID system. Obtained results indicate significant improvement over the baseline system with MFCCs plus cepstral mean subtraction (CMS), confirming the effectiveness of the proposed feature parameters for SID under reverberant mismatched conditions.
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