A successive state splitting algorithm for efficient allophone modeling

The authors propose an algorithm, successive state splitting (SSS), for simultaneously finding an optimal set of phoneme context classes, an optimal topology, and optimal parameters for hidden Markov models (HMMs) commonly using a maximum likelihood criterion. With this algorithm, a hidden Markov network (HM-Net), which is an efficient representation of phoneme-context-dependent HMMs, can be generated automatically. The authors implemented this algorithm, and tested it on the recognition of six Japanese consonants (|b|, |d|, |g|, |m|, |n| and |N|). The HM-Net gave better recognition results with a lower number of total output probability density distributions than conventional phoneme-context-independent mixture Gaussian density HMMs