Speaker Identification System Based on Multi-Classifier
Bo Wang, Yiqiong Xu, Bicheng Li · 2006
This paper presents a practical speaker recognition system based on multi-classifier structure. Multi-classifier structure overcomes the shortcomings of single classifier, such as low recognition rate, narrow application field and critical demand of environment. Additionally, multi-classifier provides a novel way of improvement of system performance. The involved classifiers include ANN (artificial neural networks), GMM (Gaussian mixed model), sub-band classifiers, etc. The input features of classifiers contain MFCC (mel frequency cepstrum coefficient), LPCC (linear prediction cepstrum coefficient). Multi-classifier confusion adopts CFM (classification figure of merit) principle as object function. In practical application, the recognition rate of the system achieves 94% in environment of super short wave (SNR 15db)