This paper examines the usefulness of a multilingual broad syllable-based framework for text-independent speaker verification. Syllabic segmentation is used in order to obtain a convenient unit for constrained and more detailed model generation. Gaussian mixture models are chosen as a suitable modelling paradigm for initial testing of the framework. Promising results are presented for the NIST 2003 speaker recognition evaluation corpus. The syllable-based modelling technique is shown to outperform a state-of-the-art baseline GMM system. A simple selective reduction of the syllable set is also shown to give further improvement in performance. Overall, the syllable based framework presents itself as valid alternative to text-constrained speaker verification systems, with the advantage of being multilingual. The framework allows for future testing of alternative modelling paradigms, feature sets and qualitative analysis.