A Preliminary Study on the Feature Distribution of Deceptive Speech Signals (Journal of Fiber Bioengineering and Informatics, 2015)
A preliminary study is conducted to compare the feature distribution between normal and deceptive\rspeech, and the results are reported in this paper. The objective of this research is to show that\rdeceptive speech may be recognized through the acoustic parameters of general speech characteristics. Six\rspeech parameters, i.e., Mel-frequency Cepstral Coefficients (MFCC), Relative Spectral Filter Perceptual\rLinear Prediction (RASTA-PLP), pitch frequency, time-domain samples, zero-crossing rate and fractal\rdimension are used in the statistics. The distributions of these parameters indicate clear differences\rbetween the two speech styles. The lowest average degree of difference for these features was 4.74%, and\rthe highest degree was over 20%. Therefore, the distribution demonstrates that there is significant\rdistinction between speech relating the truth and speech relating falsehoods. Linear Discriminant\rAnalysis (LDA) and the Gaussian Mixture Model (GMM) are used to recognize the two psychological\rstates of people's pronunciation, with accuracy above 50%. The results show that there is in fact deceptive\rinformation in speech signals and that it can be detected by pattern recognition. These findings provide\rthe theoretical basis for detecting deception in speech signals.