---

Admit it or not, it feels cringey to hear ‘machine-y’ voices in online videos. While this discomfort is harmless and can be avoided by scrolling past, deepfake audios pose serious risks and potential scams.

McAfee, a leading company in online protection, reported that using AI technology, anyone can create a fake voice using just a 3-second clip of your original voice, which has led to a rise in online scams globally.

This essentiates the use of AI-voice detectors, that actively inspect and authenticate speech, not its message per se, but also the identity of its speaker. So, let’s understand some basics of how this tech operates.

How AI Voice Detection Works?

This is how AI voice detectors analyze and categorize AI-sounds:

1. Feature Extraction-

The process starts by preprocessing the audio waveform. Input is split into chunks and gets mapped to feature representations such as:

• Mel-frequency Cepstral Coefficients (MFCCs)- numeric signals that capture human voices’ patterns in a way that a machine could understand.

• Mel-spectrograms- visual depiction of sound that shows frequency changes over time.

• Chroma and spectral contrast features- like musical pitch, peaks, or valleys in frequencies.

• Fundamental frequency (F0)- rate of vibration of your voice when you speak.

These features emphasize on audio patterns similar to human hearing and serve as the main input for learning algorithms.

2. Model Inference-

Next, these inputs are fed into a machine learning model, which could be:

• Classic algorithms, such as Support Vector Machines (SVMs) or Gaussian Mixture Models (GMMs).

• Deep learning models such as a Convolutional Neural Network (CNN) or a Transformer. A recent work by Gong & Li (2025) illustrated how combining these two layers improves deepfake detection by jointly modelling pitch and spectral attributes.

3. Classification and Decision Making

The model subsequently classifies the voice as either real or synthetic. In most cases, the system approaches this as a binary classification task (yes or no), but more sophisticated models also determine the manipulation type used to create such deepfakes, for example:

• Text-to-Speech (TTS): Synthesizes speech from text input.

• Voice Conversion (VC): A real voice with its timbre altered to mimic another person.

• Cloned Speech: AI-generated voices trained on the audio of a particular speaker.

The output from these tools could include a confidence score along with the final decision. It may also make combinatorial decisions for enhanced robustness, or return localized responses, identifying segments of audio as suspicious.

If you’re wondering, then these models are trained on datasets like ASVspoof and DFDC (Deepfake Detection Challenge) to simulate real-world manipulation cases. It generally employs loss functions such as binary cross-entropy and validation methods such as k-fold cross-validation to avoid overfitting and promote generalizability. As training datasets evolve, researchers continually refine the learning models to stay aligned with these changes.

Blend of Evolving Architectures

Despite deep learning dominating the field of AI voice detection, traditional audio features such as MFCCs and spectrograms are still used as a basis. Research, including Yi et al. (2023), confirms that these factors contribute to improving model performance. Temporal hints — such as no breath pauses in synthetic speech — prove useful detection cues as well (Fossat et al., 2024).

Currently, deep learning models like Residual Network (ResNet), Visual Geometry Group (VGG), and transformers are being developed to pick out fine-grained audio patterns. One such study to mention is DeepSonar, which monitors neuron behaviors in its neural network to identify deepfakes. Built on such architectures are platforms like DeepFake-O-Meter, which is described further.

Study Spotlight: DeepFake-O-Meter v2.0

A particular study that I came across via the internet was DeepFake-O-Meter v2.0- an open-source tool for testing multimedia, such as voice recordings, for evidence of AI tampering. Their framework consists of two unique points- Container creation and Job balancing. This platform can analyze audio inputs in just 30 seconds.

At first, it allows users to upload audio, video, or image files. It processes input audio (in formats- flac, wav, mp3) with its pretrained detection models and shows the results subsequently. Its backend could be explained in this simplified way:

• For each task, it extracts key details- username, file path, time of uploading, etc.

• It prioritizes tasks based on the user’s query submission frequency, inversely. Thus, the less frequent the query, the higher its priority. This ensures fair access for all users.

• Each required resource, like GPU availability, is checked for. Based on the user’s choice of detection models- RawNet2, LFCC-LCNN, RawNet2-Vocoder, or Whisper, etc., it launches a corresponding Docker container (self-contained application). Once the analysis is done, output is shared with the user on frontend.

It’s an ideal tool for those with limited technical knowledge or basic analytical needs. Their flexibility also allows adaptation to new models as they emerge. However, this research is an ongoing process, so updates are still in progress. Various tools like Resemble AI, Sensity.ai, and more are some available options.

What Lies Ahead?

Artificial voices and their detectors share a Tom-and-Jerry relationship. As techniques for creating AI voices improve, so do the methods for detecting them. But with their increasing capability comes the necessity for transparency, equity, and ethical use. There could be a compromise in privacy due to misuse or overuse of voice verification. False positives may be wrongly categorized as actual voices, especially in legal or journalistic contexts.

While scientists are continually adapting as synthetic speech becomes more advanced, we also should stay informed and use such tech responsibly to avoid possible digital fraud in this evolving AI era.

---

Suggestions:

1. DeepFake-O-Meter v2.0: An Open Platform for DeepFake Detection

2. How to Tell if Audio is AI-Generated? 10 Obvious Signs

Disclaimer:

Backlinks provided within this blog are intended for the reader’s further understanding only. My personal experiences and research serve as the base for the content of this blog. Despite my best efforts to keep the content current and correct, not every situation may necessarily benefit from it. Images utilized in this blog are created using Canva and SocialBlu. While making any crucial life decisions, please consult professional advice or conduct independent research. This blog does not intend to be a substitute for expert guidance; it is solely meant to be informative.