A Speech and Character Combined Recognition Engine (SCCRE) is developed for working on Personal Digital Assistants (PDA) or on mobile devices. In SCCRE, feature extraction from speech and character is carried out separately, but recognition is performed in an engine. The recognition engine employs essentially CHMM (Continuous Hidden Markov Model) structure and this CHMM consists of variable parameter topology in order to minimize the number of model parameters and reduce recognition time. This model also adopts our proposed SSMS (Successive State and Mixture Splitting) for generating context independent model. SSMS optimizes the number of mixtures through splitting in mixture domain and the number of states through splitting in time domain. When we applied our developed engine which adopts SSMS to speech recognition for mobile devices, SSMS can reduce total number of Gaussian up to 40.0% compared with the fixed parameter models at the same recognition performance. This leads that SSMS can reduce the size of memory for models to 65% and that for processing to 82%. Moreover, recognition time decreases 17% with SSMS model but still maintains the recognition rate.