@Article{JFBI-8-179,
author = {},
title = {A Preliminary Study on the Feature Distribution of Deceptive Speech Signals},
journal = {Journal of Fiber Bioengineering and Informatics},
year = {2015},
volume = {8},
number = {1},
pages = {179--193},
abstract = {A preliminary study is conducted to compare the feature distribution between normal and deceptive
speech, and the results are reported in this paper. The objective of this research is to show that
deceptive speech may be recognized through the acoustic parameters of general speech characteristics. Six
speech parameters, i.e., Mel-frequency Cepstral Coefficients (MFCC), Relative Spectral Filter Perceptual
Linear Prediction (RASTA-PLP), pitch frequency, time-domain samples, zero-crossing rate and fractal
dimension are used in the statistics. The distributions of these parameters indicate clear differences
between the two speech styles. The lowest average degree of difference for these features was 4.74%, and
the highest degree was over 20%. Therefore, the distribution demonstrates that there is significant
distinction between speech relating the truth and speech relating falsehoods. Linear Discriminant
Analysis (LDA) and the Gaussian Mixture Model (GMM) are used to recognize the two psychological
states of people's pronunciation, with accuracy above 50%. The results show that there is in fact deceptive
information in speech signals and that it can be detected by pattern recognition. These findings provide
the theoretical basis for detecting deception in speech signals.},
issn = {2617-8699},
doi = {https://doi.org/10.3993/jfbi03201518},
url = {http://global-sci.org/intro/article_detail/jfbi/4698.html}
}