EUROSPEECH '97

This paper describes a new approach to MLSSS (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 MLSSS algorithm. The tiedmixture representation results in a better state split gain, because it is able to measure diferences in the phoneme environment space that MLSSS 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 MLSSS algorithm.
Bibliographic reference. Girardi, Alexandre / Singer, Harald / Shikano, Kiyohiro / Nakamura, Satoshi (1997): "Maximum likelihood successive state splitting algorithm for tiedmixture HMNET", In EUROSPEECH1997, 119122.