Sixth International Conference on Spoken Language Processing (ICSLP 2000)
October 16-20, 2000
Spoken Word Recognition Using the Artificial Evolution of a Set of Vocabulary
Tomio Takara, Eiji Nagaki
Department of Information Engineering, University of the Ryukyus,
Senbaru, Nishihara, Okinawa, Japan
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
eective. In this paper, we propose the spoken word
recognition using the artificial evolution of a set of vocabulary.
Takara, Tomio / Nagaki, Eiji (2000):
"Spoken word recognition using the artificial evolution of a set of vocabulary",
In ICSLP-2000, vol.1, 222-225.