Abstract

This paper proposes fusion and addition techniques of vocal tract features such as Mel Frequency Cepstral Coefficients (MFCC) and Dynamic Mel Frequency Cepstral Coefficients (DMFCC) in speaker identification. Feature extraction plays an important role as a front end processing block in Speaker Identification (SI) process. Mel frequency features are used to extract the spectral characteristics of the speech such as formant frequency and the bandwidth of formant frequency. This feature estimation method leads to robust recognition performance. The Dynamic Mel frequency features are used to extract the dynamic behavior of the human vocal tract using pitch frequency. This work is focused to increase the identification accuracy with databases containing short length speech signal. Experimental evaluation is carried out on TIMIT database with 630 speakers using Gaussian Mixture Model (GMM).

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