基于ARIMA预测MFCC特征的声纹同一性鉴定方法  

Identification Method of Voiceprint Identity Based on ARIMA Prediction of MFCC Features

作　　者：王学光[1] 诸珺文 张爱新[2] WANG Xue-guang;ZHU Jun-wen;ZHANG Ai-xin(College of Criminal Justice,East China University of Political Science and Law,Shanghai 200052,China;School of Cyber Science and Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]华东政法大学刑事法学院,上海200052 [2]上海交通大学网络空间安全学院,上海200240

出　　处：《计算机科学》2022年第5期92-97,共6页Computer Science

基　　金：国家重点研发计划(2017YFB0802103)。

摘　　要：声纹识别技术的关键是从语音信号中提取具有说话人特征的语音特征参数。考虑到当下大多是运用鉴定人的经验对两段语音是否来源于同一人进行判定,在前期研究的基础上,结合MFCC特征,提出一种基于ARIMA预测的声纹同一性鉴定方法,以提高具有年份差距的检材与样本比对的准确率。此方法在Mel倒谱系数声纹同一性鉴定方法基础上,采用自回归综合移动平均季节序列作出线性最小均方估计,对声纹特征进行预测,改良了包含元音与响辅音的共振峰特性。实验证明,ARIMA时间序列的预测结果很好,且使用ARIMA改良的基于Mel倒谱系数的文本无关同一性鉴定的准确率较高,相似度在60%以上。The key of vocal pattern recognition technology is to extract the speech feature parameters with representative speaker characteristics from the speech signal.Considering that most of the contemporary determinations are made using the experience of the identifiers,combined with MFCC features,this paper proposes an ARIMA prediction-based vocal identity identification me-thod on the basis of previous study to improve the accuracy of the comparison between the examination materials with year gaps and the samples.This method uses an autoregressive integrated moving average seasonal series based on the Mel inverse spectral coefficient vocalic identity identification method,makes linear least mean square estimation,and improves the resonance peak characteristics containing vowels and loud consonants.It is demonstrated that the prediction results of ARIMA time series are good,and the accuracy of text-independent identity identification based on Mel inverse spectral coefficients using the modified ARIMA is high,with a similarity of more than 60%.

关 键 词：ARIMA预测 MEL倒谱系数 MFCC特征 同一性鉴定 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]