论文标题
使用线性和非线性预测模型的剩余信号扬声器识别
Speaker recognition using residual signal of linear and nonlinear prediction models
论文作者
论文摘要
本文讨论了剩余信号对说话者识别的有用性。结果表明，在LPCC系数上定义的措施的组合和在残留信号的能量上定义的度量的组合会导致比仅考虑LPCC系数的经典方法的改进。如果从线性预测分析获得残差信号，则改善为2.63％（错误率从6.31％降至3.68％），并且通过基于非线性预测性神经网的模型进行计算，则改善为3.68％。
This Paper discusses the usefulness of the residual signal for speaker recognition. It is shown that the combination of both a measure defined over LPCC coefficients and a measure defined over the energy of the residual signal gives rise to an improvement over the classical method which considers only the LPCC coefficients. If the residual signal is obtained from a linear prediction analysis, the improvement is 2.63% (error rate drops from 6.31% to 3.68%) and if it is computed through a nonlinear predictive neural nets based model, the improvement is 3.68%.
