Boosting the performance of connectionist large vocabulary speech recognition

Hybrid connectionist-hidden Markov model large vocabulary speech recognition has been shown to be competitive with more traditional HMM systems. Connectionist acoustic models generally use considerably less parameters than HMM's, allowing real-time operation without significant degradation of performance. However, the small number of parameters in connectionist acoustic models also poses a problem-how do we make the best use of large amounts of training data? This paper proposes a solution to this problem in which a "smart" procedure makes selective use of training data to increase performance.