ISCA Archive Eurospeech 1997
ISCA Archive Eurospeech 1997

Maximum likelihood successive state splitting algorithm for tied-mixture HMNET

Alexandre Girardi, Harald Singer, Kiyohiro Shikano, Satoshi Nakamura

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 diferences 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.


doi: 10.21437/Eurospeech.1997-57

Cite as: Girardi, A., Singer, H., Shikano, K., Nakamura, S. (1997) Maximum likelihood successive state splitting algorithm for tied-mixture HMNET. Proc. 5th European Conference on Speech Communication and Technology (Eurospeech 1997), 119-122, doi: 10.21437/Eurospeech.1997-57

@inproceedings{girardi97_eurospeech,
  author={Alexandre Girardi and Harald Singer and Kiyohiro Shikano and Satoshi Nakamura},
  title={{Maximum likelihood successive state splitting algorithm for tied-mixture HMNET}},
  year=1997,
  booktitle={Proc. 5th European Conference on Speech Communication and Technology (Eurospeech 1997)},
  pages={119--122},
  doi={10.21437/Eurospeech.1997-57},
  issn={1018-4074}
}