This paper presents our work on a speech synthesis system that utilises the trended Hidden Markov Model to represent the basic synthesis unit. We draw upon both speech recognition and speech synthesis research to develop a system that is able to synthesise intelligible and natural sounding speech. Acoustic units are clustered using the decision tree technique and speech data corresponding to these clusters is used for the training of trended Hidden Markov Model synthesis units. The overall system has been implemented in a PSOLA synthesiser and the resultant speech has been compared with that produced by a conventional diphone synthesiser to yield very encouraging results.