Recent approaches to word vector representations, eg,‘w2vec’and ‘GloVe’, have been shown to be powerful methods for capturing the semantics and syntax of words in a text. The approaches model the co-occurrences of words and recent successful applications on written text have shown how the vector representations and their interrelations represent the meaning or sentiment in the text. Most applications have targeted written language, however, in this paper, we investigate how these models port to the spoken language domain where the text is the result of (erroneous) automatic speech transcription. In particular, we are interested in the task of detecting signs of dementia in a person’s spoken language. This is motivated by the fact that early signs of dementia are known to affect a person’s ability to express meaning articulately for example when they engage in a conversation–something which is known to be cognitively very demanding. We analyse conversations designed to probe people’s short and long-term memory and propose three different methods for how word vectors may be used in a classification setup. We show that it is possible to identify dementia from the output of a speech recogniser despite a high occurrence of recognition errors.