This paper describes an integrated system to produce a composite recognition
output on distant-talking speech when the recognition results from multiplemicrophone
inputs are available. In many cases, the composite recognition result has lower
error rate than any other individual output. In this work, the composite recognition result
is obtained by applying Bayesian inference. The log likelihood score is assumed
to follow a Gaussian distribution, at least approximately. First, the distribution of the
likelihood score is estimated in the development set. Then, the confidence interval
for the likelihood score is used to remove unreliable microphone channels. Finally,
the area under the distribution between the likelihood score of a hypothesis and that
of the (N+1)st hypothesis is obtained for every channel and integrated for all channels
by Bayesian inference. The proposed system shows considerable performance
improvement compared with the result using an ordinary method by the summation of
likelihoods as well as any of the recognition results of the channels.