Ahead of the launch of iOS 11 this fall, Apple has published a research paper detailing its methods for improving Siri to make the voice assistant sound more natural, with the help of machine learning.
Beyond capturing several hours of high-quality audio that can be sliced and diced to create voice responses, developers face the challenge of getting the prosody – the patterns of stress and intonation in spoken language – just right. That’s compounded by the fact that these processes can heavily tax a processor, and so straightforward methods of stringing sounds together would be too much for a phone to handle.
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That’s where machine learning comes in. With enough training data, it can help a text-to-speech system understand how to select segments of audio that pair well together to create natural-sounding responses.
For iOS 11, the engineers at Apple worked with a new female voice actor to record 20 hours of speech in US English and generate between 1 and 2 million audio segments, which were then used to train a deep learning system. The team noted in its paper that test subjects greatly preferred the new version over the old one found in iOS 9 from back in 2015.
The results speak for themselves (ba dum tiss): Siri’s navigation instructions, responses to trivia questions and ‘request completed’ notifications sound a lot less robotic than they did two years ago. You can hear them for yourself at the end of this paper from Apple.