We discuss the role of entropy of the set of unfolding word candidates in the context of DIANA, a computational model of human auditory speech comprehension. DIANA consists of three major interacting components: Activation, Decision and Execution. The Activation component computes activations of word candidates that change over time as a function of the unfolding audio input. The resulting set of word candidate activations can be associated with an entropy that is related to difficulty of the decision when one of these candidates must be selected at time T. The paper presents the close relation between entropy measures and the between-word competition during the unfolding of the auditory stimuli, and at the end of the stimuli if no decision could be made before stimulus offset. We present a way for computing the entropy that takes into account linguistic-phonetic constraints that play a role in speech comprehension and in lexical decision experiments. Using the BALDEY data set and linear mixed effects regression models for RT, we show that entropy measures explain differences between RTs of words with different morphological structure.