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

Abstract

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%.

Keywords

• arima prediction|mel cepstrum coefficient|mfcc characteristics|identity identification