Parallel neural networks for speech recognition

Abstract

This paper presents the parallel neural networks by confidence (PNNC) and parallel neural networks by success/failure (PNNS), which generate and integrate parallel neural networks to achieve high performance on the test problem of letter recognition from string of phonemes. Our approach provides a way to create subproblems for a complex problem by partitioning the data, thus each neural network adapts to each subproblem more efficiently. Each neural network is iteratively trained on the training data which the previous neural networks could not guarantee or produce proper results. Each network works by filtering out unsatisfactory instances to pass to the next sub-network to handle. This approach provides a way, by exploring different search spaces, to handle the local minima problem without complex computations via the use of neural networks working in parallel. Experimental results show that our approach achieves improvement over the general multilayered neural network on the speech recognition problem.