Text-Dependent Speaker Verification Implemented in Matlab Using MFCC and DTW

Abstract: Even though speaker verification is a broad subject, the commercial and personal use implementations are rare. There are several problems that need to be solved before speaker verification can become more useful. The amount of pattern matching and feature extraction techniques is large and the decision on which ones to use is debatable. One of the main problems of speaker verification in general is the impact of noise. The very popular feature extraction technique MFCC is inherently sensitive to mismatch between training and verification conditions. MFCC is used in many speech recognition applications and is not only useful in text-dependent speaker verification. However the most reliable verification techniques are text-dependent. One of the most popular pattern matching techniques in text-dependent speaker verification is DTW. Although having limitations outside the text-dependent applications it is a reliable way of matching templates even with limited amount of training material. The signal processing techniques, MFCC and DTW are explained and discussed in detail along with a Matlab program where these techniques have been implemented. The choices made in signal processing, feature extraction and pattern matching  are determined by discussions of available studies on these topics. The results indicate that it is possible to program text-dependent speaker verification systems that are functional in clean conditions with tools like Matlab.