According to Gartner(“ Emerging Fraud Threats in Customer Channels,” 2024), AI-driven impersonation attacks, especially deepfake audio and synthetic identity fraud, are accelerating across enterprise contact centers.
Academic research initiatives such as ASVspoof, the leading global benchmark for synthetic speech detection, highlight the rapid advancement of voice-generation systems. But also the pressing need for robust detection methods.
Contact centers, with their high-volume and high-value voice interactions, are particularly exposed. Large enterprises may process tens of thousands of calls per day, each presenting potential opportunities for impersonation or account takeovers( ATOs).
WHY CONTACT CENTERS ARE ESPECIALLY VULNERABLE
Contact centers serve as gateways to sensitive customer data and financial transactions. Agents often have the authority to reset passwords, update personal details, authorize payments, or approve refunds.
If a malicious actor successfully impersonates a customer, the consequences can include financial loss, regulatory exposure, and reputational damage.
Historically, organizations have relied on multiple layers of protection:
• Procedural controls, such as knowledge-based authentication, passwords, and security questions.
• Voice biometrics, adopted by some large enterprises to verify callers’ identities.
• Human judgment, applied when agents notice inconsistencies or unusual conversational behavior.
While these measures remain valuable, they were developed in a world where voices were assumed to be authentic.
Synthetic audio undermines that assumption. It creates scenarios where fraudsters can mimic customers ' voices convincingly enough to bypass traditional verification methods.
THE CHALLENGE OF DETECTING DEEPFAKE VOICES
Unlike video deepfakes, which may reveal visual artifacts, synthetic voices produce subtler cues that are difficult for humans to detect. Research shows that listeners often cannot reliably distinguish real from AI-generated speech, especially in brief or noisy interactions.
Conventional detection approaches typically focus on signal-level artifacts, which are small irregularities in the audio waveform.
These methods can work in controlled environments but often fail in the diverse conditions found in real-world contact centers: multiple languages, accents, variable audio quality, and background noise.
They can be even less reliable in remote or work-from-home( WFH) contact center environments. When agents are WFH, you get uncontrolled settings, different devices, and network issues that introduce noise and distortions.
That makes it harder for traditional, signal-based systems to pick up the right cues: which is why more robust, behavior-based approaches tend to perform better. A more resilient approach looks beyond the waveform to analyze behavioral and emotional patterns in speech.
Human speech carries layers of information beyond words. Emotional cues, conversational rhythm, vocal emphasis, and micro-variations in timing all convey intent, engagement, and behavioral patterns.
While modern voice synthesis can replicate surface-level features like pitch and timbre, it struggles to reproduce the full complexity of human behavioral signals. Inconsistent emotional expression, unnatural pacing, or subtle timing errors often reveal synthetic origin if the right analytical tools are applied.
ADVANCED DETECTION
These insights underpin a new generation of detection technologies. They combine acoustic analysis with behavioral and emotional intelligence, evaluating speech for both signal-level artifacts and human behavioral patterns.
Key differentiators from the older generation of solutions, which are primarily based on voice synthesis, include:
DEEPFAKES
• Behavioral and emotional intelligence at their cores. Unlike conventional approaches, the newer systems leverage emotional and behavioral attributes of human speech to detect inconsistencies that synthetic voices struggle to replicate.
• Accuracy and robustness. Our internal benchmarks show 95 % performance on challenging datasets, surpassing older methods, which are typically 85 %– 92 % performance.
The new models are robust across multiple languages, diverse accents, and noisy environments, making them suitable for global contact center operations.
• Ultra-fast, real-time performance. Engineered for operational environments, these systems can operate as fast at 20 × real-time on standard graphics processing unit( GPU) deployments, delivering detection within 500 milliseconds for a three-second utterance.
Streaming detection identifies deepfake presences within three seconds, and the systems can flag synthetic audio from as little as two seconds of input.
( GPUs are widely used in AI and machine learning, such as for training neural networks: processing large datasets to teach models patterns in speech, images, or text. And for real-time inference: making fast predictions such as detecting deepfake voices during live calls).
Bottom line: by integrating both emotion-aware analysis and behavioral cues, the new generation of systems identify potential deepfake interactions earlier and more reliably than traditional signal-based approaches.
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