Speaker Ｒecognition under Limited Data

Abstract

Speaker recognition has attracted broad and deep research in the past few decades，and manymethods have been proposed. At present，the popular methods such as the Gaussian mixture model-Universal background model( GMM-UBM) and Gaussian mixture model-Support vector machine ( GMM-SVM) have got a better recognition result，but they all need too much training and testing data. They will suffer severe performance degradation in practical application，because their data needs always could not be satisfied. To solve this problem，a speaker recognition method based on sparse coding is presented. In the training stage，the method learns a dictionary for each speaker; and in the recognition stage，it represents test speech over each dictionary sparsely and gets scores from the reconstitution error. Experiments show that the proposed method achieves better recognition results than GMM-UBM and GMM-SVM，when the training and testing data are clean and limited.

Keywords

• speaker recognition; gaussian mixture model; support vector machine; sparse coding