This paper addresses the question how to compare reaction times computed by a computational model of speech comprehension with observed reaction times by participants. The question is based on the observation that reaction time sequences substantially differ per participant, which raises the issue of how exactly the model is to be assessed. Part of the variation in reaction time sequences is caused by the so-called local speed: the current reaction time correlates to some extent with a number of previous reaction times, due to slowly varying variations in attention, fatigue etc. This paper proposes a method, based on time series analysis, to filter the observed reaction times in order to separate the local speed effects. Results show that after such filtering the between-participant correlations increase as well as the average correlation between participant and model increases. The presented technique provides insights into relevant aspects that are to be taken into account when comparing reaction time sequences