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This paper presents an effective technique for clustering speech utterances based on their associated speaker. In attempts to determine which utterances are from the same speakers, a prerequisite is to measure the similarity of voice characteristics between utterances. Since the vast majority of existing methods evaluate the inter-utterance similarity by taking only the information from the spectrum-based features of utterance pairs into account, the resulting clusters may not be well relevant to speaker, but instead likely to the environmental conditions or other acoustic classes. To compensate for this shortcoming, this study proposes to project utterances from their spectrum-based feature representation onto a reference space trained to cover the generic voice characteristics inherently in all of the utterances to be clustered. The resultant projection vectors naturally reflect the relationships between all the utterances and are more robust against the interference from non-speaker factors. We exemplarily present three distinct implementations for reference space creation.