This paper presents an experimental study of exploiting Gaussian component information for speaker verification. The motivation of the proposed algorithm is to examine detailed component information by using individual Gaussian component’s contribution to the final output score. Analysis of component-specific score is important to understand in-depth Gaussian mixture’s impact on performance. We present a new method, called Gaussian component information based likelihood ratio (GCILR), to introduce and weight component-dependent information based on adapted Gaussian mixture models. Performance evaluations comparing our system to the well-known technique, generative likelihood ratio estimation, are provided. The paper discusses how the performance is influenced by different significance in the informative component-specific scores. Comparison experiments conducted on the NIST 2006 SRE dataset show the effectiveness of the proposed method.