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Google's Voice-Generating AI Is Now Indistinguishable From Humans (qz.com)
An anonymous reader quotes a report from Quartz: A research paper published by Google this month -- which has not been peer reviewed -- details a text-to-speech system called Tacotron 2, which claims near-human accuracy at imitating audio of a person speaking from text. The system is Google's second official generation of the technology, which consists of two deep neural networks. The first network translates the text into a spectrogram (pdf), a visual way to represent audio frequencies over time. That spectrogram is then fed into WaveNet, a system from Alphabet's AI research lab DeepMind, which reads the chart and generates the corresponding audio elements accordingly. The Google researchers also demonstrate that Tacotron 2 can handle hard-to-pronounce words and names, as well as alter the way it enunciates based on punctuation. For instance, capitalized words are stressed, as someone would do when indicating that specific word is an important part of a sentence. Quartz has embedded several different examples in their report that feature a sentence generated by AI along with a sentence read aloud from a human hired by Google. Can you tell which is the AI generated sample?
Despite choosing a low-quality human comparison (the audio fidelity is fine, but the timing and pronunciation is terrible), it is still quite obvious which is which. The synth version is slightly too clipped and the timing does not sound natural.
Slashdot: where don knuth is an idiot because he cant grasp the awesome power of php
Robocalls! :-D
Listen for the "plosives", the "p" or "b" sounds. All text-speech systems get them wrong, because they are generally programmed from recorded speech that is very frequency limited. There are reasons for that. Full digital sampling of sound uses analog-to-digital converters, limited by the digital sampling. To reduce the amount of digital storage and processing required, the designers of both recording and synthesis tools lower the sampling frequency as far as possible. They also add low bandwidth filter on the input and the outputs, to avoid sharp step functions from generating undesired artifacts on the output, and to avoid weird "beat" harmonics with the sampling frequency from confusing the recorded inputs. But the result is smearing of sharp sounds which are more rich in transients, such as "t" and "p". And dear lord, does it screw up languages with "click" sounds like Zulu.
One thing that seems to be missing from all of these is a programmatic understanding of how much air is in the lungs.
"Alexa, what is 69! (factorial)"
Listen in amazment as she rhymes off the number but then enter the uncanney valley about the time she should be taking a breath...
I am not interested in articles about life extension advancements.
Same with electric heater. The thermostat has built in AI so that it knows when to turn the heater off when it is too hot.
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Hey google, read all slashdot comments to me with a sarcastic tone.