Connectionist model combination for large vocabulary speech recognition

Abstract

Reports in the statistics and neural networks literature have expounded the benefits of merging multiple models to improve classification and prediction performance. The Cambridge University connectionist speech group has developed a hybrid connectionist-hidden Markov model system for large vocabulary talker independent speech recognition. The performance of this system has been greatly enhanced through the merging of connectionist acoustic models. This paper presents and compares a number of different approaches to connectionist model merging and evaluates them on the TIMIT phone recognition and ARPA Wall Street Journal word recognition tasks.