Fuzzy Kernel Vector Quantization with Entropy and Sectional Set for Speaker Recognition under Limited data
In case of limited data, the system performance of speaker recognition decreased significantly. To resolve this problem, it designed fuzzy kernel entropy vector quantization with sectional set to train speakers models and make identification decision in high-dimensional feature space. Entropy function can make the algorithm have clear physical meaning and avoid the unsuitable choose of fuzzy weighted exponent. Sectional set method was used to modify the membership function, which can improve the convergence speed and recognition rate. Experimental results show that for about 5s of training and Is of testing data, the performance of proposed method are 95.95％.
speaker recognition entropy funtion sectional set limited data fuzzy kernel vector quantization
Chen jian Lin lin Sun xiaoying
College of Communication Engineering Jilin University Changchun, China
国际会议
长春
英文
376-379
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）