Automatic
Speaker Recognition (ASR) is an economic tool for voice biometrics because of
availability of low cost and powerful processors. For an ASR system to be
successful in practical environments, it must have high mimic resistance, i.e.,
the system should not be defeated by determined mimics which may be either
identical twins or professional mimics. In this paper, we demonstrate the
effectiveness of Linear Prediction (LP) based features viz. Linear Prediction
Coefficients (LPC) and Linear Prediction Cepstral Coefficients (LPCC) over
filterbank based features such as Mel-Frequency Cepstral Coefficients (MFCC)
and newly proposed Teager energy based MFCC (T-MFCC) for the identification of
professional mimics in Marathi and Hindi languages.