Maximum likelihood successive state splitting algorithm for tied-mixture HMNET

This paper describes a new approach to ML-SSS (Maximum Likelihood Successive State Splitting) algorithm that uses tied-mixture representation of the output probability density function instead of a single Gaussian during the splitting phase of the ML-SSS algorithm. The tied-mixture representation results in a better state split gain, because it is able to measure di erences in the phoneme environment space that ML-SSS can not. With this more informative gain the new algorithm can choose a better split state and corresponding data. Phoneme clustering experiments were conducted which lead up to 38% of error reduction if compared to the ML-SSS algorithm.