SPEECH RECOGNITION WITH HIERARCHICAL RECURRENT NEURAL NETWORKS

Abstract

A hierarchical recurrent neural network (HRNN)for speech recognition is presented. The HRNN is trained by a generalized probabilistic descent (GPD) algorithm. Consequently, the difficulty of empirically selecting an appropriate target function for training RNNs can be avoided. Results obtained in this study indicate the proposed HRNN has the advantages of being capable of absorbing the temporal variation of speech patterns as well as possessing effective discrimination capabilities. The scaling problem of RNNs is also greatly reduced. Additionally, a realization of the system using initial/final sub-syllable models for isolated Mandarin syllable recognition is also undertaken for verifying its effectiveness. The effectiveness of the proposed HRNN is confirmed by the experimental results.