Boosting small MLPs with entropy combination improves phoneme posteriors estimation

摘要

In this paper we investigate improvements in phoneme classification and recognition using an ensemble of small size multi-layer perceptrons (MLPs) instead of a large monolithic MLP. The ensemble adopts different input context spans. It is trained using AdaBoost algorithm and output posteriors are combined according to two static and adaptive combination rules including weighting based on static classifier error and inverse entropy. The proposed method improves accuracy without increasing number of total connectionist weights. Experimental results on TIMIT corpus present promising improvements in phoneme classification and recognition rates.