A Comparative Study on Effect of Temporal Phase for Speaker Verification

In this paper, the temporal phase influence on speech signal is demonstrated through different experimental models, notably for speaker verification. Feature extraction is a fundamental block in a speaker recognition system responsible for obtaining speaker characteristics from speech signal. The commonly used short-term spectral features accentuate the magnitude spectrum while totally removing the phase spectrum. In this paper, the phase spectrum knowledge is extensively extracted and studied along with the magnitude information for speaker verification. The Linear Prediction Cepstral Coefficients (LPCC) are extracted from speech signal temporal phase and its scores are fused with Mel-Frequency Cepstral Coefficients (MFCC) scores. The trained data are modeled using the state-of-art speaker specific Gaussian mixture model (GMM) and GMM-Universal Background Model (GMM-UBM) for both LPCC and MFCC features. The scores are matched using dynamic time warping (DTW). The proposed method is tested on a fixed-pass phrase with a duration of <5 s in a speech signal. The score level fusion technique helps in the reduction of equal error rate (EER) and improves recognition rate.