Bayesian Model Combination (BAYCOM) for Improved Recognition

Combining the recognition outputs of multiple systems using methods such as ROVER or its extensions gives improved performance. However, previous approaches have been somewhat adhoc. We present BAYCOM, a Bayesian decision-theoretic approach to model combination that is optimal under given assumptions. We present recognition experiments showing that BAYCOM gives significant improvements over previous combination methods. In addition, we show that BAYCOM provides a confidence feature that gives very large improvements over previous methods for utterance rejection.