Google Duplex: Naturalistic conversational AI

Google's AI Blog has an interesting post about its new Google Duplex technology:

Today we announce Google Duplex, a new technology for conducting natural conversations to carry out “real world” tasks over the phone. The technology is directed towards completing specific tasks, such as scheduling certain types of appointments. For such tasks, the system makes the conversational experience as natural as possible, allowing people to speak normally, like they would to another person, without having to adapt to a machine.

One of the key research insights was to constrain Duplex to closed domains, which are narrow enough to explore extensively. Duplex can only carry out natural conversations after being deeply trained in such domains. It cannot carry out general conversations.

That key research insight is in fact rather old, going back to the middle of the previous century. Chess, for example, is by definition a closed domain. Duplex is currently specialized for booking hair appointments and making restaurant reservations.

There are several challenges in conducting natural conversations: natural language is hard to understand, natural behavior is tricky to model, latency expectations require fast processing, and generating natural sounding speech, with the appropriate intonations, is difficult.

When people talk to each other, they use more complex sentences than when talking to computers. They often correct themselves mid-sentence, are more verbose than necessary, or omit words and rely on context instead; they also express a wide range of intents, sometimes in the same sentence, e.g., “So umm Tuesday through Thursday we are open 11 to 2, and then reopen 4 to 9, and then Friday, Saturday, Sunday we... or Friday, Saturday we're open 11 to 9 and then Sunday we're open 1 to 9.”

In natural spontaneous speech people talk faster and less clearly than they do when they speak to a machine, so speech recognition is harder and we see higher word error rates. The problem is aggravated during phone calls, which often have loud background noises and sound quality issues.

The post has brief accounts of the technology involved. This paragraph about response latency is typical:

Also, it’s important for latency to match people’s expectations. For example, after people say something simple, e.g., “hello?”, they expect an instant response, and are more sensitive to latency. When we detect that low latency is required, we use faster, low-confidence models (e.g. speech recognition or endpointing). In extreme cases, we don’t even wait for our RNN, and instead use faster approximations (usually coupled with more hesitant responses, as a person would do if they didn’t fully understand their counterpart). This allows us to have less than 100ms of response latency in these situations. Interestingly, in some situations, we found it was actually helpful to introduce more latency to make the conversation feel more natural — for example, when replying to a really complex sentence.