Apple researchers improve Siri's ability to match commands with domains

It's no great secret that Apple's voice assistant has plenty of room for improvement. The Cupertino company is aware of this -- in June, it debuted an improved neural text-to-speech model capable of delivering a more natural-sounding voice without the use of samples. And in a newly published research paper on the preprint server Arxiv.org, a team of Apple scientists describe an approach for selecting training data for Siri's domain classifier -- the component that chooses whether a person's command relates to, say, their calendar rather than their alarms -- that leads to a substantial error reduction with only a small percentage of examples. As the researchers explain, Siri processes speech to suss out the intended domain with a classifier called the Domain Chooser, which helps identify a given user's intent. Once an utterance is matched to one of the over 60 defined domains, a component called the Statistical Parser assigns a parse label to each part of the utterance, after which the domain and parse labels predicted by the Domain Chooser and Statistical Parser are mapped into an intent representation that kicks off the appropriate action.