Comparative Study of Boosting and Non-Boosting Training for Constructing Ensembles of Acoustic Models

This paper compares the performance of Boosting and nonBoosting training algorithms in large vocabulary continuous speech recognition (LVCSR) using ensembles of acoustic models. Both algorithms demonstrated significant word error rate reduction on the CMU Communicator corpus. However, both algorithms produced comparable improvements, even though one would expect that the Boosting algorithm, which has a solid theoretic foundation, should work much better than the non-Boosting algorithm. Several voting schemes for hypothesis combining were evaluated, including weighted voting, un-weighted voting and ROVER.