RESTRICTED BOLTZMANN MACHINE SUPERVECTORS FOR SPEAKER RECOGNITION

The use of Restricted Boltzmann Machines (RBM) is proposed in this paper as a non-linear transformation of GMM supervectors for speaker recognition. It will be shown that the RBM transformation will increase the discrimination power of raw GMM supervectors for speaker recognition. The experimental results on the core test condition of the NIST SRE 2006 corpus show that the proposed RBM supervectors will achieve a comparable performance to i-vectors. Furthermore, the combination of RBM supevectors and i-vectors in the score level improves the performance of the i-vector approach by more than 10% in terms of EER.