Thanks to advancements in machine learning, voice cloning has made significant progress in recent years, resulting in some of the most impressive text to speech solutions to date. Among the most important developments is zero shot, which has been creating waves in the tech sector. This article will introduce zero-shot voice cloning and how it has transformed the industry.
Zero-shot Machine Learning Explained
The objective of voice cloning is to replicate a speaker's voice by synthesizing their tone and color using only a small amount of recorded speech. In other words, voice cloning is a state-of-the-art technology that uses artificial intelligence to create a voice that resembles a specific person. This technology distinguishes three main voice cloning processes:
One-shot Learning
One-shot learning means the model is trained on only one picture of something new, but it should still be able to recognize other images of the same thing.
Few-shot Learning
Few-shot learning is when a model is shown a few pictures of something new and can recognize similar things even if they look a little different.
Zero-shot Learning
Zero-shot learning is teaching a model to recognize new objects or concepts that it has not been previously trained on by using a dataset, such as VCTK, to describe them. This is when the model is taught to recognize new things without pictures, examples, or other training data. Instead, you give it a list of characteristics or features that describe the new item.
What is Voice Cloning?
Voice cloning is replicating a speaker's voice using machine learning techniques. The objective of voice cloning is to reproduce the speaker's tone using only a small amount of their recorded speech. In voice cloning, a speaker encoder turns a person's speech into a code that can later be transformed into a vector using speaker embedding. That vector is then used to train a synthesizer, also known as a vocoder, to create a speech that sounds like the speaker's voice. The synthesizer takes the speaker embedding vector and a mel spectrogram, a visual representation of the speech signal, as input. This is the baseline process for voice cloning. It then produces a waveform output, which is the actual sound of the synthesized speech. This process is typically done using machine learning techniques such as deep learning. Additionally, it can be trained using a variety of datasets and metrics to evaluate the quality of the generated speech. Voice cloning can be used for various applications such as:
- Voice conversion - the ability to change a recording of one person's voice to sound like another person spoke it.
- Speaker verification - when someone says they are a certain person, and their voice is used to check if it's true.
- Multispeaker text to speech - a creation of the speech from the printed text and keywords
Some popular voice cloning algorithms include WaveNet, Tacotron2, Zero-shot Multispeaker TTS, and Microsoft’s VALL-E. Also, many other open-source algorithms can be found on GitHub, offering excellent final results. Additionally, if you're interested in learning more about voice cloning techniques, the ICASSP, Interspeech, and IEEE International Conference are the right places for you.
Zero-shot Learning in Voice Cloning
A speaker encoder is used to extract speech vectors from training data to achieve zero-shot voice cloning. These speech vectors can then be used for signal processing of speakers that haven’t been included in the training datasets before, also known as unseen speakers. This can be accomplished by training a neural network using a variety of techniques, such as:
- Convolutional models are neural network models employed to solve image classification problems.
- Autoregressive models can forecast future values based on past values.
One of the challenges of zero-shot voice cloning is to ensure that the synthesized speech is of high quality and sounds natural to the listener. To address this challenge, various metrics are used to evaluate the quality of the speech synthesis:
- Speaker similarity measures how similar the synthesized speech is to the original target speaker's speech patterns.
- Speech naturalness refers to how natural the synthesized speech sounds to the listener.
The actual data from the real world, which is used to teach and evaluate AI models, is called the ground truth reference audio. This data is used for training and normalization. In addition, style transfer techniques are employed to enhance the model's ability for generalization. Style transfer involves using two inputs - one for the main content and the other for the style reference - to improve the model's performance with new data. In other words, the model is better able to handle new situations.
See the Latest Voice Cloning Technology at Work with Speechify Studio
Speechify Studio’s AI voice cloning allows you create a custom AI version of your own voice—perfect for personalizing narration, building brand consistency, or adding a familiar touch to any project. Simply record a sample, and Speechify’s advanced AI models will generate a lifelike digital replica that sounds just like you. Want even more flexibility? The built-in voice changer allows you to reshape existing recordings into any of Speechify Studio's 1,000+ AI voices, giving you creative control over tone, style, and delivery. Whether you’re refining your own voice or transforming audio for different contexts, Speechify Studio puts professional-grade voice customization at your fingertips.
FAQ
What is the point of voice cloning?
Voice cloning aims to produce high-quality, natural-sounding speech that can be utilized in various applications to improve communication and interaction between humans and machines.
What is the difference between voice conversion and voice cloning?
Voice conversion involves modifying one person's speech to sound like another person, whereas voice cloning creates a new voice that resembles a specific human speaker.
What software can clone someone's voice?
Numerous options are available, including Speechify, Resemble.ai, Play.ht, and many others.
How can you detect a faked voice?
One of the most common techniques to identify audio deepfake is spectral analysis, which involves analyzing an audio signal to detect distinctive voice patterns.
