Microsoft claims to have made significant progress in getting its automated speech-recognition systems—as used in services such as Cortana—to do their thing as accurately as people can.
The company's researchers have produced a new paper describing the advances they have made since last year, when they said their systems had achieved "human parity" with a 5.9% error rate in transcribing audio conversations.
Since that happened, researchers at rival IBM achieved an error rate of 5.5%—and while they were at it, they said their research showed real human parity would involve an error rate as low as 5.1%.
Now, Microsoft's researchers say they've reached that new milestone, meaning they're claiming for the second time to have developed speech-recognition systems with a human-level accuracy rate.
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"Reaching human parity with an accuracy on par with humans has been a research goal for the last 25 years," the researchers said in a blog post.
The researchers said the improvement to Microsoft's systems came from tweaks to its neural net-based acoustic and language models. Neural nets, or networks, are programs that take their inspiration from the workings of organic brains. These artificial brains provide a key building block for current work on "artificial intelligence," and are used in speech and image recognition, among many other applications.
"Moreover, we strengthened the recognizer’s language model by using the entire history of a dialog session to predict what is likely to come next, effectively allowing the model to adapt to the topic and local context of a conversation," Microsoft's researchers added.