Improved training using semi-hidden Markov models in speech recognition

The idea of the semi-hidden Markov model (SHMM) is described, central to which is a modified training process. A modification to the conventional Baum-Welch algorithm is the kernel of the SHMM, where states are classified into types, reflecting fundamentally different speech signal characteristics. A preset supervisory function is introduced to the Baum-Welch algorithm and biases the training by reflecting the fitness of local signal characteristics to different state types. A simple example is described, using transient and quasi-stationary state types, which is found to be successful in E-set recognition.<
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