Deep Residual Neural Networks for Unknown Voice Spoof Detection
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Solution Overview
Problem
Conventional voice biometric systems are vulnerable to spoofing attacks from advanced speech synthesis technologies, failing to detect unknown spoofing techniques and lacking generalization ability.
Innovation Solution
A neural network architecture is employed for spoof detection, utilizing embedding extractors to differentiate between voiceprint and spoofprint features, with a large margin cosine loss function to maximize variance between genuine and spoofed classes, and minimize intra-class variance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional voice biometric systems are used for speaker recognition, then the system is simple to implement and operates efficiently, but the system becomes vulnerable to spoofing attacks from advanced speech synthesis technologies
Solution Approach 1:
The system segments the feature extraction process into two distinct pathways: voiceprint extraction for genuine speaker verification and spoofprint extraction for detecting synthesized speech. This segmentation allows the system to specialize each extractor for its specific task, improving spoofing detection capability while maintaining manageable complexity through functional decomposition
Solution Approach 2:
The patent introduces spoofprint embeddings as an intermediary representation that captures artifacts specific to synthesized speech. This intermediary layer acts as a mediator between the raw audio input and the final spoofing detection decision, enabling the system to identify spoofed audio without requiring direct comparison against known spoofing methods
2Adaptability or versatility
If conventional voice matching is used, then the system operates efficiently with existing methods, but the system becomes obsolete due to improvements in speech synthesis tools capable of fooling these systems
Solution Approach 1:
The neural network architecture is designed with universal components that can handle both genuine speaker verification and spoofing detection through a unified training framework. The multi-task learning approach allows the system to generalize across different spoofing techniques by learning common artifacts in synthesized speech, rather than requiring specialized detectors for each attack type
Solution Approach 2:
The system performs preliminary extraction of spoofprint features during the enrollment phase, creating a baseline representation of spoofing artifacts for each speaker. This preliminary action enables the system to quickly compare incoming audio against established spoofing patterns, reducing real-time processing requirements while maintaining high generalization ability
3Measurement precision
If the neural network extracts features for both voiceprint and spoofprint, then the system achieves better spoof detection, but the processing complexity and computational requirements increase
Solution Approach 1:
The system extracts only the specific features necessary for spoofing detection through the spoofprint extractor, separating these from the general voiceprint features. This selective extraction focuses computational resources on the most discriminative artifacts of synthesized speech, achieving high detection accuracy while minimizing unnecessary processing of irrelevant audio characteristics
Solution Approach 2:
The patent merges the voiceprint and spoofprint extraction processes into a single neural network architecture that operates simultaneously on the same audio input. This unified approach shares computational resources and intermediate representations between the two extractors, reducing overall energy consumption compared to running separate independent systems
Data Source
AI summary
Embodiments described herein provide for systems and methods for implementing a neural network architecture for spoof detection in audio signals. The neural network architecture contains a layers defining embedding extractors that extract embeddings from input audio signals. Spoofprint embeddings are generated for particular system enrollees to detect attempts to spoof the enrollee's voice. Optionally, voiceprint embeddings are generated for the system enrollees to recognize the enrollee's voice. The voiceprints are extracted using features related to the enrollee's voice. The spoofprints are extracted using features related to features of how the enrollee speaks and other artifacts. The spoofprints facilitate detection of efforts to fool voice biometrics using synthesized speech (e.g., deepfakes) that spoof and emulate the enrollee's voice.