The Lincoln robust HMM recognizer has been converted from a single Ganssian or Gaussian mixture pdf per state to tied mixtures in which a single set of Gaussians is shared between all states. There were some initial difficulties caused by the use of mixture pruning  but these were cured by using observation pruning. Fixed weight smoothing of the mixture weights allowed the use of word-boundary-context-dependent triphone models for both speaker-dependent (SD) and speakerindependent (SI) recognition. A second-differential observation stream further improved SI performance but not SD performance. The overall recognition performance for both SI and SD training is equivalent to the best reported according to the October 89 Resource Management test set. A new form of phonetic context model, the semiphone, is also introduced. This new model significantly reduces the number of states required to model a vocabulary.