AI voice cloning, is done through modern cutting-edge techniques of machine learning to mimic and even build human-like voices. The first step is to collect a large amount of voice data, usually anywhere from an hour or two up to days worth when spoken. For example, a good voice model requires about 10–20 hours of audio data. This data is used to train deep neural networks instead of how a person’s voice sounds, as this 2013 study on MIT Technology Review explains.
At its core, AI voice cloning technology is built upon text-to-speech (TTS) systems that harness the power of AI algorithms to convert written words into spoken language. These systems are capable with WaveNet technology from DeepMind, a process that creates human-like sounds and rhythms by studying the waveform data. As per a review published by Wired in 2024, WaveNet-based models generate audio that is over 50% more natural-sounding than traditional TTS techniques.
In order to achieve an accurate voice cloning, AI systems apply a technique called speaker embedding. Constructed from a recording of the individual, this representation assigns numbers to different vocal qualities (voice timber, pitch frequencies and timing characteristics) which are unique between people. iSpeech Voice cloning system utilizes speaker embeddings for 90% likeness with the original voice, as shown in a study of 2024
It takes an enormous amount of data and compute resources to actually train these models. Companies such as Resemble AI work on the cloud to process and analyze voice data utilizing GPUs for heavier compute. That level of processing power goes beyond that available to many commercial voice cloning solutions, reports TechCrunch in 2024.
After trained, the AI voice cloning system can synthesize speech from text in a given speaker’s style using patterns learned. To achieve this, the technique consists in generating new audio segments that were compatible with each of the characteristics extracted from the voice model so that an AI could create speech sounding very alike to those which would originate directly from a human speaker. For example, a 2024 analysis published by Forbes results in other newer voice cloning techniques to produce only less than five percent error margin which is indeed very good.
Anyone who is contemplating the ai voice cloning experience might struggle to connect with these core technologies and recognize how subpar from real human speech produced by their often higher system. In addition, innovations in machine learning and neural networks are increasingly expanding the potential for voice synthesis.