In this paper we introduce the demiphone as a contextual phonetic unit for continuous speech recognition. A phone is divided into two parts: a left demiphone that accounts for the left side coarticulation and a right demiphone that copes with the right side context. This new unit discards the dependence between the effects of both side contexts, but provides a better training of the transition between phones. The demiphone can be seen as a heuristic clustering of states that allows a more smoothed training of hidden Markov models and additionally supplies a simple way to create unseen triphones. We report experimental evidence that demiphones outperform the usual combination of triphones, right-side and left-side biphones and monophones.