In this study, we used the Moore type discrete Ergodic HMM. The number of states (categories) of the Ergodic HMM was set at 4, the same as the number of speakers.
It is well known that the Baum-Welch algorithm runs toward local maxima, Therefore, the speaker classification rate seems greatly depends on the initial parameters. Accordingly, the initial parameters were calculated in the following three ways.
As the initial HMM parameters
, true values were given
to the initial state probability
, the state transition
probability
and the symbol output probability
.
Symbol output probability
was given its true value. Initial
state probability
and the state transition probability
were given uniform probabilities.
The initial state probability
and the state transition
probability
were given uniform probabilities. The symbol
output probability
was assigned random probabilities. Note
that the Baum-Welch learning algorithm does not work if a uniform
probability is given to
.