Robust Q Features for Speaker Identification

In this paper, a nonlinear AM-FM speech model is used to extract robust features for speaker identification. The proposed features measure the amount of amplitude and frequency modulation that the commonly used linear source-filter model and the Mel frequency cepstral coefficients (MFCC) feature fails to capture. From the short time estimates of the frequency and bandwidth, a novel set of features is proposed. The robustness and discriminability of the features is investigated in comparison with the MFCC features using the clean speech corpus from TIMIT database and noise from the NOISEX-92 database. The proposed feature set provides significant improvement in the identification accuracy over the MFCC features under mismatched training and testing environments. The results show that better speaker identification rates are attainable under mismatched conditions especially at low signal-to-noise ratio (SNR).