Investigating Causal Cues: Strengthening Spoofed Audio Detection with Human-Discernible Linguistic Features

TL;DR

This study explores how human-discernible linguistic features can improve spoofed audio detection by analyzing causal relationships, highlighting the importance of integrating human knowledge into AI models for better accuracy.

Contribution

It introduces a causal discovery approach using sociolinguistic features to enhance spoofed audio detection and demonstrates the value of human knowledge in AI model development.

Findings

Causal models show linguistic features aid in spoof detection

Incorporating human knowledge improves AI detection performance

Causal inference supports training humans for better discernment

Abstract

Several types of spoofed audio, such as mimicry, replay attacks, and deepfakes, have created societal challenges to information integrity. Recently, researchers have worked with sociolinguistics experts to label spoofed audio samples with Expert Defined Linguistic Features (EDLFs) that can be discerned by the human ear: pitch, pause, word-initial and word-final release bursts of consonant stops, audible intake or outtake of breath, and overall audio quality. It is established that there is an improvement in several deepfake detection algorithms when they augmented the traditional and common features of audio data with these EDLFs. In this paper, using a hybrid dataset comprised of multiple types of spoofed audio augmented with sociolinguistic annotations, we investigate causal discovery and inferences between the discernible linguistic features and the label in the audio clips, comparing the findings of the causal models with the expert ground truth validation labeling process. Our findings suggest that the causal models indicate the utility of incorporating linguistic features to help discern spoofed audio, as well as the overall need and opportunity to incorporate human knowledge into models and techniques for strengthening AI models. The causal discovery and inference can be used as a foundation of training humans to discern spoofed audio as well as automating EDLFs labeling for the purpose of performance improvement of the common AI-based spoofed audio detectors.