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A limited feedback time-delay neural network

Neural networks with time delays offer the potential of providing massive parallelism and adaptation, and can provide excellent discrimination for temporal signal processing tasks. In a time delay neural network (TDNN), the shift-invariance capability required in speech recognition is achieved by explicitly making time-shifted copies of the inputs and internal responses and linking their corresponding weights. In this paper, we propose a limited feedback TDNN (LFB-TDNN), which feedbacks only those errors with significant magnitudes over a limited time span during the semi-batch back-propagation learning. Simulations show that the LFB-TDNN achieves favorable performance in isolated speech recognition tasks when compared with the standard TDNN, the discrete density and the continuous density hidden Markov models.