Speaker recognition based on SOINN and incremental learning Gaussian mixture model

Abstract

Gaussian Mixture Models has been widely used in speaker recognition during the last decades. To deal with the dynamic growth of datasets, initial clustering problem and achieving the results of clustering effectively on incremental data, an incremental adaptation method called incremental learning Gaussian mixture model (IGMM) is proposed in this paper. It was applied to speaker recognition system based on Self Organization Incremental Learning Neural Network (SOINN) and improved EM algorithm. SOINN is a Neural Network which can reach a suitable mixture number and appropriate initial cluster for each model. First, the initial training is conducted by SOINN and EM algorithm only need a limited amount of data. Then, the model would adapt to the data available in each session to enrich itself incrementally and recursively. Experiments were taken on the 1st speech separation challenge database. The results show that IGMM outperforms GMM and classical Bayesian adaptation in most of the cases.