Chaotic recurrent neural networks and their application to speech recognition

Abstract

A new Chaotic Recurrent Neural Network (CRNN) is proposed to improve the learning ability and the application to the speech recognition of Korean spoken digits and Korean monosyllables is discussed. The learning performance of the CRNN is compared to that of the RNN and the influence of refractory parameters on learning is considered. The MFCC (Mel-Frequency Cepstrum Coefficients) is used as an input feature for learning and recall. Speech data are acquired by using A/D board composed of AD574A chip with 8 kHz sampling rate and 12 bit resolution. In the experiment on Korean monosyllables, the classification process and the segmentation process are executed for recognizing many syllables.