A.I. Speech Sounds More Human than Ever
With its stilted phrasing and flat tone, computer-generated speech is still pretty robotic.
The technique brings us closer to a world where computers not only understand everything we say, but are able to communicate as naturally and smoothly as a real person.
Some of the best computer-generated speech comes from a method called concatenative text-to-speech (TTS for short), which knits together short, recorded phrases that were spoken by a human and saved to a database.
But although each individual phrase sounds humanlike, the overall dialogue sounds unnatural because the combined phrases can't be varied to represent inflection or emotion.
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There is also a method called parametric TTS that generates audio using signal processing algorithms known as vocoders. But the results are also robotic.
Google's WaveNet is different. It works like other deep neural systems and learns from the audio samples fed into its system.
Like all learning, there is a period of training. During the training phase, WaveNet is fed sound recorded from human speakers. Using statistics, the A.I. makes a prediction about which audio piece it needs next based on the previous one.
The animation above demonstrates how the inputs move through various layers of the neural network as the A.I. predicts the next sound in order to produce an output.
After training, the researchers sampled the network to generate synthetic utterances. The results were analyzed and new values were generated that could be fed back into WaveNet to improve its predictions. Overtime, the speech got better and better.
WaveNet is so good at predicting the sound it needs, it can even generate its own raw audio samples. This means it can synthesize speech -- and even music -- without any human inputting the sound. To hear some examples, click over to the Deep Mind site and scroll down.
It's just a matter of time before machines can understand our every word and respond with a human-sounding voice.
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