Learning Bayesian Networks
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This paper describes an integrated system to produce a composite recognition output on distant-talking speech when the recognition results from multiple microphone 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.