سال انتشار: ۱۳۸۴
محل انتشار: یازدهمین کنفرانس سالانه انجمن کامپیوتر ایران
تعداد صفحات: ۴
H. R. Sadegh Mohammadi – Iranian Research Institute for Electrical Eng., P. O. Box 16765-1899,Tehran, Iran
R. Saeidi – Electrical Engineering Department, Iran University of Science and Technology,Narmak, Tehran, Iran
Training of high order Gaussian mixture models (GMM) on large dataset in one stage requires considerable amount of processing power and storage requirement which may not be either feasible or available in many cases. While training of such GMMs in several stages reduces the computational and memory costs; this normally results in a sub-optimum GMM compared to the one which entirely is trained in a single stage. In this paper a new method for optimization of the multi-stage trained GMMs is proposed in the context of speaker verification framework. Experimental results show that the optimized GMMs trained by incorporation of the proposed algorithm improves the performance of the GMM based speaker verification system.