In this paper, we focus on distributed speaker recognition, a technique in which quantized feature parameters are sent to a server, as with distributed speech recognition. The Gaussian mixture model , the traditional method used for speaker recognition, is trained using the maximum likelihood approach. The GMM has output probability functions with continuous density functions. It is difficult to fit continuous density functions to quantized data. To overcome this problem, we propose a novel speaker recognition technique which does not need speaker model training. The proposed method directly calculates the distance between a set of quantized feature parameters of registered speech and a set of quantized feature parameters of test speech. To measure distance, we use Earth Mover’s Distance (EMD). The EMD has recently been successfully applied to image retrieval. We conduct text-independent speaker identification experiments using the proposed method. When compared to results using the traditional GMM, the proposed method yielded relative error reductions of 80% for quantized data.