Hill-Climbing Feature Selection For Multi-Stream Asr

We performed automated feature selection for multi-stream (i.e., ensemble) automatic speech recognition, using a hill-climbing (HC) algorithm that changes one feature at a time if the change improves a performance score. For both clean and noisy data sets (using the OGI Numbers corpus), HC usually improved performance on held out data compared to the initial system it started with, even for noise types that were not seen during the HC process. Overall, we found that using Opitz's scoring formula, which blends single-classifier word recognition accuracy and ensemble diversity, worked better than ensemble accuracy as a performance score for guiding HC in cases of extreme mismatch between the SNR of training and test sets.Our noisy version of the Numbers corpus, our multi-layer-perceptron-based Numbers ASR system, and our HC scripts are available online.