Rodríguez-Liñares L, Garciá-Mateo C
Departamento de Tecnoloxías das Communicacións, ETSE Telecommunicación, Universidade de Vigo, Spain.
J Acoust Soc Am. 2001 Jan;109(1):385-9. doi: 10.1121/1.1331679.
In this paper, a speaker recognition system that introduces acoustic information into a Gaussian mixture model (GMM)-based recognizer is presented. This is achieved by using a phonetic classifier during the training phase. The experimental results show that, while maintaining the recognition rate, the decrease in the computational load is between 65% and 80% depending on the number of mixtures of the models.
本文提出了一种将声学信息引入基于高斯混合模型（GMM）的说话人识别系统。这是通过在训练阶段使用语音分类器来实现的。实验结果表明，在保持识别率的同时，计算量的减少幅度在65%至80%之间，具体取决于模型的混合数量。
