Stream weight tuning in dynamic Bayesian networks

In this paper we present a family of algorithms for estimating stream weights for dynamic Bayesian networks with multiple observation streams. For the 2 stream case, we present a weight tuning algorithm optimal in the minimum classification error sense. We compare the algorithms to brute-force search where feasible, as well as to previously published algorithms and show that the algorithms perform as well as brute-force search and outperform previously published algorithms. We test the stream weight tuning algorithm in the context of speech recognition with distinctive feature tandem models. We analyze how the criterion used for weight tuning differs from the standard word error rate criterion used in speech recognition.