We present a new stochastic model for the energy and duration of phone segments ivhich takes account of the speech rate, the loudness of the signal and the effects of stress and pre-pausal lengthening and we show how the block Viterbi decoding algorithm can be used to integrate it with phone-based HMM speech recognizers. The model has been implemented on an isolated-word data-base and a preliminary experiment gives a modest improvement in word recognition accuracy.