Abstract
In this paper, we provide theoretical results on the problem of optimal stream weight selection for the multi-stream classification problem. It is shown, that in the presence of estimation or modeling errors using stream weights can decrease the total classification error. The stream weights that minimize classification estimation error are shown to be inversely proportional to the single-stream pdf estimation error. It is also shown that under certain conditions, the optimal stream weights are inversely proportional to the single-stream classification error. We apply these results to the problem of audio-visual speech recognition and experimentally verify our claims. The applicability of the results to the problem of unsupervised stream weight estimation is also discussed.
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