ISCA Archive ICSLP 2000
ISCA Archive ICSLP 2000

Spoken word recognition using the artificial evolution of a set of vocabulary

Tomio Takara, Eiji Nagaki

Hidden Markov models (HMMs) are widely used for automatic speech recognition. However, there is a problem still unresolved, i.e. how to design the optimal structure of the HMM. As an answer to this problem, we proposed the application of a genetic algorithm (GA) to search out such an optimal structure, and we showed this method to be effective for isolated word recognition. In these applications, the evolutions occurred at each word class independently. However, many isolated word recognition systems are performed using a set of vocabulary. Therefore, the artificial evolution using the vocabulary set is thought to be more e ective. In this paper, we propose the spoken word recognition using the artificial evolution of a set of vocabulary.


Cite as: Takara, T., Nagaki, E. (2000) Spoken word recognition using the artificial evolution of a set of vocabulary. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 1, 222-225

@inproceedings{takara00_icslp,
  author={Tomio Takara and Eiji Nagaki},
  title={{Spoken word recognition using the artificial evolution of a set of vocabulary}},
  year=2000,
  booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
  pages={vol. 1, 222-225}
}