Using aggregation to improve the performance of mixture Gaussian acoustic models

Abstract

This paper investigates the use of aggregation as a means of improving the performance and robustness of mixture Gaussian models. This technique produces models that are more accurate and more robust to different test sets than traditional cross-validation using a development set. A theoretical justification for this technique is presented along with experimental results in phonetic classification, phonetic recognition, and word recognition tasks on the TIMIT and Resource Management corpora. In speech classification and recognition tasks error rate reductions of up to 12% were observed using this technique. A method for utilizing tree-structured density functions for the purpose of pruning the aggregated models is also presented.