ALDAS: Audio-Linguistic Data Augmentation for Spoofed Audio Detection

TL;DR

ALDAS introduces an AI framework for auto labeling linguistic features to improve spoofed audio detection, reducing reliance on manual annotation while maintaining performance gains.

Contribution

This paper presents ALDAS, a novel AI-based method for auto labeling linguistic features, enhancing spoofed audio detection without extensive manual annotation.

Findings

ALDAS improves detection performance with auto labeled features.

Labels generated by ALDAS are validated by experts.

Auto labeling achieves comparable results to manual annotation.

Abstract

Spoofed audio, i.e. audio that is manipulated or AI-generated deepfake audio, is difficult to detect when only using acoustic features. Some recent innovative work involving AI-spoofed audio detection models augmented with phonetic and phonological features of spoken English, manually annotated by experts, led to improved model performance. While this augmented model produced substantial improvements over traditional acoustic features based models, a scalability challenge motivates inquiry into auto labeling of features. In this paper we propose an AI framework, Audio-Linguistic Data Augmentation for Spoofed audio detection (ALDAS), for auto labeling linguistic features. ALDAS is trained on linguistic features selected and extracted by sociolinguistics experts; these auto labeled features are used to evaluate the quality of ALDAS predictions. Findings indicate that while the detection enhancement is not as substantial as when involving the pure ground truth linguistic features, there is improvement in performance while achieving auto labeling. Labels generated by ALDAS are also validated by the sociolinguistics experts.