A successive state and mixture splitting algorithm for an on-line speech and character combined recognition system

摘要

A Speech and Character Combined Recognition System (SCCRS) is developed for working on PDA (Personal Digital Assistants) or on mobile devices. In SCCRS, feature extraction for speech and for 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 to 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. The recognition results show that the proposed SSMS method can reduce the total number of Gaussian up to 40.0% compared with the fixed parameter models at the same recognition performance in speech recognition system.