Frequency Domain Linear Prediction-Based Robust Text-Dependent Speaker Identification

Abstract

Speaker identification is a biometric technique of determining an unknown speaker's identity among a number of speakers using distinguish latent information of uttered speech. Crime investigation, security control, telephone banking and trading, and information reservation are some applications of this technique. Frequency Domain Linear Prediction (FDLP) is a time-frequency-based feature has been derived using 2-D autoregressive model. This feature was constructed from sub-bands short frame energies estimation. FDLP has been used in this study to propose a robust text-dependent speaker identification technique. The clean features were used to obtain speaker behavioural model. Support vector machine has been used to train the proposed method. This presented study was tested in both clean and noisy conditions to validate the method extensively. The proposed method got significant improved performance over all traditional methods performances in noisy conditions. The obtained performance was indicated; the proposed method was very robust to noises and showed consistent performance irrespective to noises.