Synthetic Speech Detection for Voice Bot DoS Mitigation
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Solution Overview
Problem
The increasing sophistication of generative AI technologies, particularly agentic AI systems, poses a significant threat by enabling large-scale denial of service (DoS) attacks through the generation and transmission of synthetic audio signals, overwhelming enterprise communication systems and making detection difficult.
Innovation Solution
A computing system employs machine-learning architectures to analyze inbound audio signal data and metadata, identifying acoustic artifacts and behavioral anomalies to detect synthetic speech and mitigate DoS attacks by updating a blocklist of source identifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional audio analysis methods are used, then system complexity is low, but detection precision of synthetic speech is insufficient
Solution Approach 1:
The patent segments the detection process into multiple independent analysis streams: acoustic feature extraction, spectral analysis, temporal pattern recognition, and metadata examination. Each stream processes specific aspects of the audio signal separately, then combines results for comprehensive detection. This modular approach increases detection precision while managing system complexity through organized modularity.
Solution Approach 2:
The patent employs a composite detection system that combines multiple analysis methods (acoustic analysis, spectral analysis, temporal analysis) with machine learning classifiers. This composite approach integrates different detection techniques to achieve higher precision than any single method could provide alone, while the structured composition keeps complexity manageable.
2Measurement precision
If machine-learning architectures are deployed for real-time detection, then detection precision improves, but processing time increases
Solution Approach 1:
The patent performs preliminary feature extraction and preprocessing of audio signals before they reach the machine learning classifiers. Acoustic features, spectral characteristics, and temporal patterns are extracted in advance, organizing the data into standardized formats. This preliminary action reduces the computational burden during real-time detection, maintaining precision while reducing processing time.
Solution Approach 2:
The patent replaces complex mechanical processing with optimized algorithms and hardware-accelerated computing. Machine learning models are implemented using efficient computational frameworks that leverage GPU acceleration and optimized linear algebra operations, substituting brute-force mechanical computation with smarter, faster computational methods that maintain precision while reducing time loss.
3Reliability
If comprehensive acoustic feature extraction is performed, then detection reliability improves, but computational power requirements increase
Solution Approach 1:
The patent extracts only the most discriminative acoustic features relevant to synthetic speech detection, rather than analyzing all possible audio characteristics. Features such as spectral centroid, zero-crossing rate, and formant frequencies are selectively extracted as they provide the highest reliability for detection. This selective extraction maintains detection reliability while reducing computational power requirements by focusing on critical features only.
4Reliability
If velocity thresholds are applied to blocklist updates, then system reliability against DoS attacks improves, but ease of operation decreases
Solution Approach 1:
The patent implements automated blocklist management with velocity threshold monitoring that operates autonomously. The system automatically detects excessive call velocities, evaluates them against predefined thresholds, and updates the blocklist without manual intervention. This self-service approach improves system reliability against DoS attacks while maintaining ease of operation, as the automation handles the complex decision-making process that would otherwise require manual configuration and monitoring.
Data Source
AI summary
Embodiments disclosed herein include software processes and of machine-learning architectures for detecting and mitigating against synthetic speech instances. A computer analyzes audio speech data and metadata received with contact events associated with source identifiers. The computer executes machine-learning architecture(s) that determine whether the contact events likely include human-generated speech or machine-generated synthetic speech. The computer may determine the likelihood that contact events represent a DoS attack launched by a source device, by analyzing behavior features in metadata associated with the source identifier. The computer determines whether the contact events originated from the source user device having the source identifier launched a DoS attack and, if so, may update a blocklist. The blocklist may be stored in a database and includes one or more source identifiers that should be rejected or blocked at the current or inbound contact event or at future contact events for the particular source identifiers.