Nonlinear analysis of natural vs. HTS-based synthetic speech

Abstract

Many investigations on speech nonlinearities have been carried out and these studies provide strong evidences to support nonlinear system modelling of speech production. The nonlinear characteristics that these studies point to are analogous to chaotic systems. This paper aims to provide evidence of chaotic nature of speech signal and use it for feature extraction to distinguish synthetic and natural speech. The feature used to extract chaos is Lyapunov Exponent (LE). The synthetic speech is found to have higher values of LE in comparison with natural speech. We propose a new feature based on LE for detection of synthetic speech. The synthetic speech used is from Hidden Markov Model (HMM)-based speech synthesis system (HTS) trained using low resource Indian language-Gujarati. This work may find its application for improving robustness of speaker verification (SV) systems against imposture attack using synthetic speech.