Multiple Microphone Speaker Recognition System for Second Language Based on Biomimetic Pattern Recognition with Big Data Fusion

Abstract

The speaker recognition problem for second language based on biomimetic pattern recognition with big data fusion by multiple microphone has been addressed in this paper. Biomimetic pattern recognition is a new machine learning algorithm which can be used to study the geometric characteristics of a large number of sample points in high-dimensional space. Machine learning is an important way for computer to realize intelligence. The development of artificial intelligence can not be separated from the support of machine learning. Big data Unifield architecture of Hadoop system integrates machine learning and big data processing. Several speaker feature extraction methods of big data are described. Cepstrum and Δcepstrum can offer a significant computational advantage in the eigenvalue problem in result of enhancing correct rate. The big data fusion framework of neural network multiple microphone verification system is presented; in particular, a neural network constructed by multiple value neural network algorithm of big data fusion is also proposed for adjusting the parameters. The experimental results of big data fusion using multiple microphone speaker recognition system for second language illustrate the effectiveness of the proposed biomimetic pattern recognition method.