Comparative studies of two neural network architectures for modeling of human speech production

A new neural network architecture called the parallel self-organizing consensual neural network (PSCNN) is introduced. Comparative studies of this architecture and the backpropagation (BP) neural network in modeling of human speech production are discussed. The PSCNN consists of a number of self-organizing modules which operate in parallel, during training as well as testing. While these modules can be selected to be any type of neural network, they are chosen to be simple single layer delta rule networks, in this paper. A simplified language, similar to English, is constructed for the purpose of evaluating the performance of the two networks. The behavior of both networks were studied in early stages of training and compared to that of a normal human child in early stages of speech development. In general, the PSCNN network performed considerably better than the BP network in the experiments. Both networks had some errors, but those of PSCNN resembled more closely the human error patterns. In addition, the PSCNN is easy to implement in real-time, parallel architectures. Analysis of certain types of errors indicate that future networks may need some properties of both PSCNN and BP networks