The Estimating Optimal Number of Gaussian Mixtures Based on Incremental k-means for Speaker Identification
Younjeong Lee, Ki Yong Lee, Joohun Lee · 2006
Gaussian mixture model (GMM) is generally used to estimate the speaker model from speech for speaker identification. In this paper, we propose the method that estimates the optimal number of Gaussian mixtures based on incremental k-means for speaker identification. In the proposed method, the initialization with the optimal number of mixtures is done by adding dynamically the number of mixtures one by one until the mutual relationship between any two mixtures becomes dependent. The effectiveness of the proposed method is proven by two experiments.