Inspired by successful work in forensic speaker identification, this work presents a higher level system for text-independent speaker recognition by means of the temporal trajectories of formant frequencies in linguistic units. Feature extraction from unit-dependent trajectories provides a very flexible system able to be applied in different scenarios. At a fine-grained level, it is possible to provide a calibrated likelihood ratio per linguistic unit under analysis (extremely useful in applications such as forensics), and at a coarse-grained level, the individual contributions of different units can be combined to obtain a more discriminative single system with high potential for combination with short term spectral systems. With development data being extracted from NIST SRE 2004 and 2005 datasets, this approach has been tested on NIST SRE 2006 1side-1side task, English-only male trials, consisting of 9,720 trials from 219 speakers. Remarkable results have been obtained for some single units from extremely short segments of speech, and the combination of several units leads to a relative improvement of 17.2% on EER when fusing with an i-vector system.