This paper presents a novel phoneme-based approach to enrollment utterance selection for speaker identification. Unlike conventional strategies that ignore linguistic diversity, our method explicitly maximizes phoneme coverage in the enrollment set, yielding more representative and robust speaker profiles. We demonstrate that increasing phoneme diversity directly improves speaker embeddings and identification accuracy, even under real-world speech variability. Experiments on the Vietnam-Celeb dataset with the state-of-the-art ECAPA-TDNN model show that our approach boosts identification accuracy from 93.6% to 95.7% and F1-score from 95.5% to 96.1%, relative to standard selection methods. Remarkably, these gains are achieved with fewer enrollment utterances, substantially reducing user effort. Analysis reveals a near-linear relationship between phoneme coverage and classification performance, highlighting phoneme diversity as a critical factor for effective enrollment. These findings underscore the practical value of our method for building more accurate, efficient, and user-friendly speaker identification systems.