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We present an approach to model the temporal evolution of audio descriptors using Segmental Models (SMs). This method allows to segment a signal as a sequence of primitives, constituted by a set of trajectories defined by the user. This allows one to explicitly model the time duration of primitives, and to take into account the time dependence between successive signal frames, contrary to standard Hidden Markov Models. We applied this approach to a database of violin playing. Various types of glissando and dynamics variations were specifically recorded. Our results shows that our approach using Segmental Models provides a segmentation that can be easily interpreted. Quantitatively, the Segmental Models performed better than standard implementation of Hidden Markow Models.