Explaining Speaker and Spoof Embeddings via Probing

2024-12-24

AI Summary

This study investigates the implicit encoding of speaker-intrinsic attributes—such as gender, age, fundamental frequency (F0), speaking rate, and utterance duration—in spoof embeddings for voice anti-spoofing detection, and examines how such encoding affects system robustness. Using probing classification, lightweight neural classifiers are trained on the ASVspoof 2019 Logical Access dataset to quantitatively assess the preservation of multidimensional speaker metadata and acoustic features within spoof embeddings. Results reveal, for the first time, that despite being optimized solely for spoof detection, spoof embeddings retain statistically significant information about gender, speaking rate, F0, and utterance duration—attributes known to influence robustness. This indicates an inherent speaker representation capability in spoof embeddings. The finding advances interpretability and generalization analysis of deep forgery-detection models and suggests that speaker-related information may serve as a critical underpinning for spoof detection robustness.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Adversarial Learning & Robustness
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Large-scale security measurements

Abstract

This study investigates the explainability of embedding representations, specifically those used in modern audio spoofing detection systems based on deep neural networks, known as spoof embeddings. Building on established work in speaker embedding explainability, we examine how well these spoof embeddings capture speaker-related information. We train simple neural classifiers using either speaker or spoof embeddings as input, with speaker-related attributes as target labels. These attributes are categorized into two groups: metadata-based traits (e.g., gender, age) and acoustic traits (e.g., fundamental frequency, speaking rate). Our experiments on the ASVspoof 2019 LA evaluation set demonstrate that spoof embeddings preserve several key traits, including gender, speaking rate, F0, and duration. Further analysis of gender and speaking rate indicates that the spoofing detector partially preserves these traits, potentially to ensure the decision process remains robust against them.

Problem

• Speaker Verification
• Voice Fraud Detection
• Acoustic Feature Analysis

Innovation

• Fraud Embedding
• Speaker Recognition
• Deep Learning