Speaker Recognition Using Radial Basis Function Neural Networks
A text-dependent speaker recognition system base on Radial Basis Function (RBF) neural network is presented. A two-stage recognition approach is proposed, in which the speaker-cohort model and the gender model are integrated to give the decision. The speaker recognition system has been evaluated in terms of both speaker verification and closed-set speaker identification. The results clearly indicate that the two-stage procedure is able to improve the overall performance of the speaker recognition system.
KeywordsRadial Basis Function Hide Neuron Radial Basis Function Neural Network Radial Basis Function Network Dynamic Time Warping
Unable to display preview. Download preview PDF.
- 1.Ariyaeeinia AM, Sivakumaran P (1997). Comparison of VQ and DTW classifiers for speaker verification, In: European Conference on Security and Detection, 1997. ECOS 97.,, pp 142–146.Google Scholar
- 2.ChiWei Che; Qiguang Lin, Dong-Suk Yuk (1996) An HMM approach to text-prompted speaker verification. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol 2, pp 673–676.Google Scholar
- 4.Mak MW, Allen WG, Sexton, GC (1993) Speaker identification using radial basis functions. In: Third International Conference on Artificial Neural Networks, pp 138–142.Google Scholar
- 5.Finan RA, Sapeluk AT, Damper RI (1996) Comparison of multilayer and radial basis function neural networks for text-dependent speaker recognition. In: IEEE International Conference on Neural Networks, 1996, vol 4, pp 1992–4997.Google Scholar
- 6.Zhang WD, Mak MW, He X (2000) Two-stage scoring method combining world and cohort models for speaker verification. In: IEEE International Conference on Acoustics, Speech and Signal Processing, 2000, vol 2, pp 1193–1196.Google Scholar