In order to build psycholinguistic models of processing difficulty and evaluate these models against human data, we need highly accurate language models. Here we specifically consider surprisal, a word’s predictability in context. Existing approaches have mostly used n-gram models or more sophisticated syntax-based parsing models; this largely does not account for effects specific to semantics. We build on the work by Mitchell et al. (2010) and show that the semantic prediction model suggested there can successfully predict spoken word durations in naturalistic con versational data.
An interesting finding is that the training data for the semantic model also plays a strong role: the model trained on in-domain data, even though a better language model for our data, is not able to predict word durations, while the out-of-domain trained language model does predict word durations. We argue that this at first counter-intuitive result is due to the out-of-domain model better matching the "language models" of the speakers in our data.