We apply boosting techniques to the problem of word error rate minimisation in speech recognition. This is achieved through a new definition of sample error for boosting and a training procedure for hidden Markov models. For this purpose we define a sample error for sentence examples related to the word error rate. Furthermore, for each sentence example we define a probability distribution in time that represents our belief that an error has been made at that particular frame. This is used to weigh the frames of each sentence in the boosting framework. We present preliminary results on the well-known Numbers 95 database that indicate the importance of this temporal probability distribution.