Text-independent speaker recognition based on recurring expectation maximum adjustment algorithm
Xinmin Cheng · Shengxue jishu · 2008
For the targeted model in speaker recognition, there is fatal default of fantastic array about the expectation maximum algorithm, and although the maximum likelihood estimate can ' t appear fantastic array, but there is lower rate of recognition. A recurring expectation maximum adjustment algorithm is proposed to utilize the maximum likelihood estimate to gain the initial models. These initial models are modified according to controlling adjustment rate with every model in the expectation maximum algorithm. Then more optimal models can be obtained. The results of the experiments show that the adjustment algorithm can conquer fantastic array well, and improve rate of recognition.