Faked Speech Detection with Zero Prior Knowledge

TL;DR

This work tackles blind speech forgery detection under zero prior knowledge, proposing a 3-layer neural network trained on 26 features to distinguish real from mimicked audio. It demonstrates high effectiveness, achieving a test ROC AUC around $0.974$ (English) and $0.963$ (Mixed) with EERs near $0.053$–$0.057$, and about $94\%$ test accuracy, surpassing human performance. The study also shows language dependency and mitigates it by training on multilingual data (English+Arabic), illustrating practical applicability for multilingual forensics. Overall, the approach offers a viable, reference-free pre-processing tool for audio forensics and impersonation detection, with potential for mobile deployment and extension to more languages and datasets.

Abstract

Audio is one of the most used ways of human communication, but at the same time it can be easily misused to trick people. With the revolution of AI, the related technologies are now accessible to almost everyone, thus making it simple for the criminals to commit crimes and forgeries. In this work, we introduce a neural network method to develop a classifier that will blindly classify an input audio as real or mimicked; the word 'blindly' refers to the ability to detect mimicked audio without references or real sources. We propose a deep neural network following a sequential model that comprises three hidden layers, with alternating dense and drop out layers. The proposed model was trained on a set of 26 important features extracted from a large dataset of audios to get a classifier that was tested on the same set of features from different audios. The data was extracted from two raw datasets, especially composed for this work; an all English dataset and a mixed dataset (Arabic plus English) (The dataset can be provided, in raw form, by writing an email to the first author). For the purpose of comparison, the audios were also classified through human inspection with the subjects being the native speakers. The ensued results were interesting and exhibited formidable accuracy, as we were able to get at least 94% correct classification of the test cases, as against the 85% accuracy in the case of human observers.

Figures (4)

• Figure 1: The proposed model.
• Figure 2: Model Summary
• Figure 3: Training and testing losses vs the number of epochs.
• Figure 4: Receiver Operating Characteristic (ROC) curves.