Humans are able to recognise a word before its acoustic realisation is complete. This in contrast to conventional automatic speech recognition (ASR) systems, which compute the likelihood of a number of hypothesised word sequences, and identify the words that were recognised on the basis of a trace back of the hypothesis with the highest eventual score, in order to maximise efficiency and performance. In the present paper, we present an ASR system, SpeM, based on principles known from the field of human word recognition that is able to model the human capability of ‘early recognition’ by computing word activation scores (based on negative log likelihood scores) during the speech recognition process. Experiments on 1463 polysyllabic words in 885 utterances showed that 64.0% (936) of these polysyllabic words were