Why use the AI audio detector?
Check whether audio has been generated by artificial intelligence or by a person
Audio deepfake detection
Our AI detector allows you to detect Artificial intelligence (AI) generated sounds such as phone calls, video calls, or deepfakes. It uses a unique approach combining a range of techniques so you can detect ai generated audio in real-time, identify the model used and can detect ai generated audio from ‘unseen models’.
Our approach is world leading and has very low error rates compared to other approaches. You can detects AI generated sounds that has been manipulated by people or with online tools such as voice cloning. Our AI sound detector also converts audio to text. We look at the text analysis to decide whether the audio has been scripted by AI.
Visualisation Tool
AI Aware’s visualisation tool shows the proportion of AI-generated content and whether it occurs in blocks or is dispersed. This allows you to contextualise the findings and understand the significance of AI-generated input.
Our AI sound detector grades each section of audio as AI or human generated content, showing its consistency with either. This means that users can get an overall view of a document and understand what is AI and what is human created.
How AI audio detection works
AI audio detection works by analysing the acoustic and linguistic properties of a sound recording to determine whether it was produced by a human voice or synthesised by an artificial intelligence model. Modern AI voice generation tools(including text-to-speech engines, voice cloning software, and generative speech models) leave subtle but detectable traces in the audio they produce.
AI Aware’s audio deepfake detector examines multiple layers of a recording simultaneously. At the acoustic level, it analyses patterns in pitch variation, breath placement, micro-pauses, and vocal resonance that differ between human speakers and AI-generated voices. At the linguistic level, it evaluates the structure, rhythm, and scripting of speech, since AI-generated content often displays distinct patterns in sentence construction and word choice.
Unlike detection tools that rely solely on machine learning classifiers trained on known AI models, AI Aware combines machine learning with non-machine learning signal analysis. This hybrid approach is what enables the platform to detect audio from previously unseen AI models. Such a system is a critical capability as new voice generation tools emerge constantly. The result is a robust, low false-positive detection system that works accurately across a wide range of audio sources, including phone calls, video calls, podcasts, and recorded interviews.
Benefits of AI-generated audio detection for companies
The threat posed by AI-generated audio is not limited to any one industry. As voice cloning and deepfake audio technology becomes more accessible, the need for reliable AI audio detection spans a growing range of sectors.
Financial services is among the most targeted industries by AI audio scams. Fraudsters use cloned voices of executives or trusted contacts to authorise fraudulent transfers. This is a form of social engineering that has already cost organisations millions of pounds globally. An AI audio detector integrated into video call workflows can flag synthetic voices in real time before financial damage occurs.
Legal and compliance teams increasingly need to verify the authenticity of recorded evidence, witness testimonies, and communications. AI-generated audio submitted as evidence presents serious risks to the integrity of legal proceedings, making detection tools an essential part of due diligence.
Media organisations and publishers face reputational risks from distributing content that contains fabricated quotes or synthetic voices presented as real. This means that audio verification is becoming a standard part of editorial fact-checking.
HR and recruitment teams are encountering AI-generated voices in remote interviews and candidate submissions, raising questions about identity verification and authenticity.
Education providers need to assess whether student-submitted audio or video assignments are genuinely the work of the individual. As AI voice tools become easier to use, academic integrity policies need to extend to audio content.
Whether you are protecting your business from fraud, verifying evidence, or maintaining content standards, AI Aware’s audio deepfake detector provides the accuracy and transparency you need.
Frequently asked questions
What is an audio deepfake?
An audio deepfake is a voice recording that artificial intelligence has generated or manipulated. This includes entirely synthetic voices that text-to-speech AI models create, as well as real voices that voice conversion tools have cloned or altered. Audio deepfakes can be difficult to detect by ear, particularly as AI voice technology continues to improve.
What is voice cloning and can the detector identify it?
Voice cloning is a technique where AI creates a synthetic copy of a specific person’s voice, typically from a small sample of their real speech. AI Aware’s detector identifies cloned voices, fully synthetic voices, and audio that people have post-processed or manipulated after generation.
How is AI audio detection different from traditional audio forensics?
Traditional audio forensics focuses on detecting editing artefacts, background noise inconsistencies, and metadata anomalies in recordings. AI audio detection goes further by specifically identifying the signatures left by AI generation processes. These are patterns in speech synthesis, model-specific artefacts, and linguistic structures associated with scripted or AI-written content. AI Aware combines both approaches for comprehensive analysis.
How accurate is AI Aware’s audio deepfake detector?
AI Aware’s detector achieves very low false positive and false negative rates as it used large volumes of varied audio output in testing. Its hybrid approach (a combination of machine learning with non-machine learning signal analysis) gives it an accuracy advantage over tools that rely on machine learning alone, particularly when encountering audio from newer or previously unseen AI models.
Can the detector identify which AI model generated the audio? Yes. Where possible, the detector can identify the specific AI model used to generate the audio. This is useful for investigations, compliance reporting, and understanding the source and nature of synthetic content.
Does the detector work on real-time video and phone calls?
Yes. AI Aware’s audio detector can analyse audio from live video calls and phone calls in real time. It suits environments that demand immediate detection, such as financial transactions or identity verification calls. Contact us to discuss how to integrate this.
What types of audio files can the detector analyse ?
The detector supports analysis of a wide range of audio and video formats. This includes recorded phone calls, video call recordings, voice messages, podcasts, and other audio files. If you have specific format requirements, AI Aware’s team can advise on compatibility.
Is there a free trial available? Yes. You can access a free trial of AI Aware’s audio deepfake detector. Start with no long-term commitment.
Market leading AI audio detector
AI Aware has built it’s audio deepfake detector combining unique non-machine learning approaches with large scale machine learning to identify the differences between AI-generated and human created sounds.
We use various signals from human and AI content such as logic, linguistic structure and creativity to differentiate between the two.