How to detect an AI-generated voice on live calls

AI-generated voice fraud is increasingly being used in vishing attacks to impersonate people, obtain credentials, and access sensitive systems, prompting demand for real-time call authentication. The post distinguishes fixed-voice text-to-speech, cloned text-to-speech, and real-time voice conversion, arguing that effective detection should identify structural audio patterns rather than merely recognize known generator fingerprints. It describes Resemble AI’s proposed trust stack, combining waveform-based detection with forensic explainability, voice identity matching, watermark verification, and fraud-pattern scoring, while noting that detection assesses audio authenticity rather than intent or consent. Because PSTN codecs, transcoding, cellular compression, packet loss, and call-processing features degrade useful audio signals, the article presents internal codec tests showing high but variable accuracy, particularly under aggressive compression. It outlines two Telnyx-integrated deployment models: streaming analysis that can return a result after four seconds and enable actions such as transfer or hang-up during a call, and post-call auditing that aggregates results across recordings to reduce false positives. Suggested applications include high-risk financial and account-access calls, regulated-sector recording workflows, outbound AI voice verification, and contact-center fraud monitoring, alongside compliance considerations such as recording consent, STIR/SHAKEN’s limited scope, EU requirements for machine-readable AI-content marking, and evidence-grade logging.

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