The term voice changer (also known as voice enhancer) refers to a device which can change the tone or pitch of or add distortion to the user’s voice, or a combination and vary greatly in price and sophistication. A kazoo or a didgeridoo can be used as a makeshift voice changer, though it can be difficult to understand what the person is trying to say.
An audio deepfake (also known as voice cloning) is a type of artificial intelligence used to create convincing speech sentences that sound like specific people saying things they did not say. This technology was initially developed for various applications to improve human life. For example, it can be used to produce audiobooks, and also to help people who have lost their voices (due to throat disease or other medical problems) to get them back. Commercially, it has opened the door to several opportunities. This technology can also create more personalized digital assistants and natural-sounding text-to-speech as well as speech translation services.
Audio deepfakes, recently called audio manipulations, are becoming widely accessible using simple mobile devices or personal computers. These tools have also been used to spread misinformation using audio. This has led to cybersecurity concerns among the global public about the side effects of using audio deepfakes, including its possible role in disseminating misinformation and disinformation in audio-based social media platforms. People can use them as a logical access voice spoofing technique, where they can be used to manipulate public opinion for propaganda, defamation, or terrorism. Vast amounts of voice recordings are daily transmitted over the Internet, and spoofing detection is challenging. Audio deepfake attackers have targeted individuals and organizations, including politicians and governments. In early 2020, some scammers used artificial intelligence-based software to impersonate the voice of a CEO to authorize a money transfer of about $35 million through a phone call. According to a 2023 global McAfee survey, one person in ten reported having been targeted by an AI voice cloning scam; 77% of these targets reported losing money to the scam. Audio deepfakes could also pose a danger to voice ID systems currently deployed to financial consumers.
Audio deepfakes, recently called audio manipulations, are becoming widely accessible using simple mobile devices or personal computers. These tools have also been used to spread misinformation using audio. This has led to cybersecurity concerns among the global public about the side effects of using audio deepfakes, including its possible role in disseminating misinformation and disinformation in audio-based social media platforms. People can use them as a logical access voice spoofing technique, where they can be used to manipulate public opinion for propaganda, defamation, or terrorism. Vast amounts of voice recordings are daily transmitted over the Internet, and spoofing detection is challenging. Audio deepfake attackers have targeted individuals and organizations, including politicians and governments. In early 2020, some scammers used artificial intelligence-based software to impersonate the voice of a CEO to authorize a money transfer of about $35 million through a phone call. According to a 2023 global McAfee survey, one person in ten reported having been targeted by an AI voice cloning scam; 77% of these targets reported losing money to the scam. Audio deepfakes could also pose a danger to voice ID systems currently deployed to financial consumers.
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And AI voice change, also known as voice synthesis or voice transformation, is a technology that uses artificial intelligence and machine learning techniques to modify or generate human-like speech. It can be used for various purposes, such as creating natural-sounding text-to-speech (TTS) systems, changing the pitch or tone of a voice, or even imitating specific speakers. And it can application to audio deepfake Here’s how AI voice change typically works:
- Data Collection: The first step in creating an AI voice change system is to gather a substantial amount of audio data. This data includes recordings of a human speaker’s voice, covering a wide range of sounds, words, and sentences. The quality and diversity of this training data are crucial for achieving realistic and accurate voice transformations.
- Feature Extraction: Once the data is collected, the AI system extracts various acoustic features from the audio recordings. These features include pitch, intonation, phonetic content, and other aspects of speech that are essential for voice synthesis.
- Training a Neural Network: Deep learning techniques, such as recurrent neural networks (RNNs) or convolutional neural networks (CNNs), are often used to train models for voice change. These neural networks learn to map the extracted features from the source voice to the target voice.
- Voice Conversion: During the voice conversion process, the AI model takes the acoustic features of a source voice and transforms them into the corresponding features of a target voice. This involves changing the pitch, speaking style, and other characteristics of the voice while retaining the phonetic content and linguistic context.
- Synthesis: The transformed acoustic features are used to synthesize speech in the target voice. Text-based TTS systems can also be integrated, allowing users to convert text input into speech in the desired voice.
- Post-processing: Post-processing techniques are applied to the synthesized speech to make it sound more natural and human-like. This may involve smoothing out transitions, adjusting prosody, or adding subtle variations to the voice to avoid robotic-sounding output.
- Real-time or Batch Processing: Depending on the application, AI voice change can be used for real-time voice conversion, where speech is transformed as it is spoken, or for batch processing, where pre-recorded audio is converted to a different voice.
AI voice change technology has applications in various fields, such as speech synthesis for virtual assistants, dubbing and voice-over services, and voice imitation for entertainment or security purposes. It has become increasingly sophisticated and realistic in recent years, thanks to advances in deep learning and the availability of large datasets for training. However, ethical considerations related to voice cloning and impersonation have also arisen, and it’s important to use this technology responsibly and with consent.
How does voice cloning work?
The process typically involves recording a large amount of audio data from the target voice, which is then used to train a machine-learning model. Once the model is trained, it can generate new audio clips in the target voice by synthesizing the sounds and patterns learned from the original recordings. In recent years, the necessary training time has been shortened from hours to minutes thanks to improvements in TTS algorithms (e.g. Vector TTS). These algorithms can even produce audio with real human emotions like laughter and anger (like the recently released Peregrine model by Play.ht).
The Voice of Reason: How AI Voices Can Be a Benchmark for other Generative AI
The text-to-image AI revolution has exploded in recent months, but it has also brought with it a number of issues that need addressing. These include giving artists the right to opt out of training data sets, copyrighting/watermarking AI creations, and concerns about the use of AI to generate deepfakes and inappropriate content. It’s hard to predict the future of text-to-image technology, but one area of generative AI that can be seen as a benchmark is AI voices.
The Voice AIs are more advanced and older than any Image AI, why don’t we see any personal videos dubbed with the beautiful voice of Scarlett Johansson or other celebrities? As you may know, voice cloning poses many threats. The human voice is not only personal data but also biometric information that is as unique as a fingerprint. The term “cloning,” which is associated with Star Wars and unethical biological experiments, probably hasn’t helped build trust in the technology either. As a result, many voice cloning apps (e.g., Resemble.ai) are only available upon request. Others use a smart solution by only allowing the owner of a voice to have it cloned by reading a consent within the app (e.g., Descript). It’s interesting to see how the same technology that can be used to produce deepfakes has been adapted to verify the biometric features of a human voice and protect us against threats. This approach is a great example of how we can cope with the “wild west” happening in the world of AI-generated images.
For now, AI voices seem to be some of the safest and most advanced AI out there, ready to read your books, stories, and soon, sing songs about your brands. Stay tuned.
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