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The voices produced by artificial intelligence have come a long way over the past few decades. In the early days of speech synthesis, the computerized voices sounded robotic and unnatural. These early text-to-speech systems in the 1970s and 80s, like DECtalk and MacinTalk, had a choppy rhythm and lack of intonation that made them unpleasant to listen to for more than a few minutes.
As AI research advanced in the 1990s, speech synthesis started to sound more human. Systems like AT&T Natural Voices used Concatenative synthesis, splicing together recordings of real people"s voices. This allowed for more natural inflection and expression. However, the audio clips were short and often didn"t blend together seamlessly. The result was speech that was intelligible but still had an artificial quality.
The 2000s saw the rise of statistical parametric and unit selection synthesis. These methods allowed systems to analyze and model the acoustic properties of human voices. By mathematically representing the spectral envelope, fundamental frequency, and other elements, AI voices could emulate the nuances that make each person"s voice unique. Vocaloids like Yamaha"s Vocaloid 2 engine produced singing voices that were nearly indistinguishable from humans.
In the past decade, advances in deep learning brought another leap forward. Models like Google"s Tacotron 2 and DeepMind"s WaveNet learned to generate speech waveforms from scratch. By training neural networks on thousands of hours of speech data, they built an understanding of the complex audio textures of human voices. The result was AI systems capable of remarkably natural and expressive synthetic speech.
Today, the latest AI voice models use generative adversarial networks (GANs) to sharpen vocal realism. Companies like Lyrebird and Sonantic have created virtual voice actors that capture the distinctive timbre and style of specific individuals. Their demos reveal just how far AI voices have progressed.