Parallel sequential running neural network and its application to automatic speech recognition
Huaiyu Zeng, Tiecheng Yu · 1992
A novel parallel sequential running neural network (PSRNN) is developed. It consists of subnets of the same construction. The subnet was trained by different tokens sequentially. The neural network makes recognition by subnets in the order of training. PSRNN performs better than multilayer perceptron (MLP). It can learn adaptively and expand easily. The authors applied PSRNN to the work of speaker-independent isolated word recognition. The system was trained by 45 persons to recognize ten Chinese digits. Performance was 97% when tested by another 10 persons.>