In this paper, we present a non-parametric speaker identification method using Earth Mover’s Distance (EMD) designed for text-indepedent speaker identification and its evaluation results for CCC Speaker Recognition Evaluation 2006, organized by the Chinese Corpus Consortium (CCC) for the th International Symposium on Chinese Spoken Language Processing (ISCSLP 2006). EMD based speaker identification (EMD-IR) was originally designed to be applied to a distributed speaker identification system, in which the feature vectors are compressed by vector quantization at a terminal and sent to a server that executes a pattern matching process. In this structure, we had to train speaker models using quantized data, then we utilized a non-parametric speaker model and EMD. From the experimental results on a Japanese speech corpus, EMD-IR showed higher robustness to the quantized data than the conventional GMM technique. Moreover, it achieved higher accuracy than GMM even if the data was not quantized. Hence, we have taken the challenge of CCC Speaker Recognition Evaluation 2006 using EMD-IR. Since the identification tasks defined in the evaluation were on an open-set basis, we introduce a new speaker verification module. Evaluation results show that EMD-IR achieves 99.3 % Identification Correctness Rate in a closed-channel speaker identification task.