The answer is yes, no, and better get used to it. Microsoft hasn’t actually said how many aspects of the Xbox One are going to work, but the demo it gave at its campus in Redmond, Wash. Tuesday contained some solid hints on the particulars of its voice control. To wake up the device and launch live TV, play a game or do anything at all with it, users will first have to say “Xbox on.”

That’s what people who work on speech recognition call “hot words” – easily recognizable phrases that can be detected by a system without too much effort. Once a user says that magic word or phrase, the actual speech recognition kicks into high gear.

That means that the Xbox One continuously listens for someone to say “Xbox on,” and that everything else that’s spoken is automatically disregarded. Listening for these hot words is done locally and doesn’t require much in terms of system resources. For example, there’s no need to record anything, since all that matters are the hot words. But once those words are uttered, the Xbox One is going to use advanced speech recognition to figure out what users are actually talking about.

Again, Microsoft hasn’t said exactly how this is going to work, but a spokesperson told me that some of the personalization offered by the device is “one of the benefits of Xbox One being connected to and powered by the cloud.” I’d expect that the same is true for speech recognition, much in the same way that Google uploads everything you say to its servers when you use voice search on your Android phone.

The use of hot wording to wake up technology from a state of low-level listening to launch active speech recognition isn’t new. It’s also at work in Google Glass, where users get the device’s attention by saying “okay glass.” Google Now simply uses “Google” as a hot word to launch voice input. And the Xbox 360 starts to accept voice commands once users yell “Xbox” at the device’s Kinect sensor.

The difference between how the Xbox 360 and the Xbox One approach voice recognition isn’t so much about technology, even though Xbox users probably hope that the new iteration is going to work better. What makes people feel uncomfortable is that the Xbox One, and with it its microphone, are meant to be always on.

However, the always-on microphone of the Xbox One is just a sign of things to come. Voice input is going to become a key component of a growing number of internet-connected devices and appliances in your home, car and office, and many of them will use hot words to switch from low-level listening to active speech recognition.

In fact, you are likely looking at one of those devices right now: Laptops, tablets and mobile phones all contain microphones, and they’re all waiting to become hot words-aware any day now. Google just demonstrated how it is going to add hot wording to search on the desktop at last week’s Google I/O conference, allowing users to start a voice search query by simply saying “okay Google” without touching a single button.

Of course, all of this doesn’t mean that there are no privacy issues around hot wording and always-on microphones. Companies should make it clear how exactly they’re using the technology as it is becoming more widely distributed, and there should always be a way to opt out and rely on alternative input methods. It may also be a good idea to indicate to users  when exactly a device is reverting back from active speech recognition to a state of passive listening. But I’d expect that most consumers quickly get used to the constantly running mic, always listening for those magic words.

Image courtesy of Flickr user visual.dichotomy.

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Nuance on Monday announced it’s expanding its speech interface expertise into yet another industry, advertising, launching a new service for app developers called Voice Ads. Yes, we’re talking about talking ads here – well, at least listening ads. Whether the ad responds via text, video or spoken word is entirely up to the ad creator. The key is that advertisers now have a new way of engaging with their audience rather than just mere splashy images, said Mike McSherry, VP of Advertising and former CEO of Swype, the predicative keyboard company Nuance acquired in 2011.

Say Jeff Daniels is the spokesman for your product. Instead of just flashing up an image of Daniels holding your widget in the confined space of an app banner ad, your ad could let you virtually ask Daniels questions about it and your company. Answers are pre-recorded, of course, but as we’ve witnessed with the depth and variety of conversational answers Apple has given Siri (try asking Siri if she believes in God), a committed ad agency could have some fun with the medium.

McSherry said that mobile app developers can embed Voice Ad technology into their apps through an SDK, which will allow ad networks like JumpTap, Millennial and Ad Marvel to serve up the new formats in those apps, either as banner or interstitial ads. Nuance is currently working with three agency partners to develop the new ads: Digitas, OMD and Leo Burnett.

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Joyride is creating a voice user interface for the smartphone designed to function much like the voice command-and-control systems in your car. Founders Jeff Chen and James Zhang created Skyvi, a voice-assistant app for Android that received 5 million downloads. With the help of $1 million in seed funding, they’re now building similar voice-interaction technology into Joyride with the aim of creating a game, entertainment and education platform that is fully hands-free. Though you could use its app anywhere, Joyride CEO Chen said, it’s most useful within the restrictive confines of the car.

Joyride plans to launch an Android app in the next few months — right now it’s in a private beta — and its first service will be a trivia game Chen described as “a hands-free version of Words With Friends.” But Joyride plans to layer more games and services on top of the app, and will eventually invite outside developers to embed their own applications into the Joyride app framework.

“Think of Joyride as a portal,” Chen said. “It’s an enabling technology intended for people to build things on top of.” Joyride would be the overarching brand and its voice technology would not only power the apps themselves, but allow you to navigate between them. The first Joyride apps will be games, but Chen hopes to layer on any manner of service that lends itself to voice interaction, from audiobooks and music streaming to highly interactive language-learning apps.

Joyride makes use of the car’s stereo system through an auxiliary jack or a Bluetooth connection, though the app doesn’t actually tap into any connected car software or OS — it’s just using the speakers. From there, all interactions are done via voice — it makes use of Google’s speech API — requiring  no actual physical input, Chen said. The approach also has the advantage of making the app car agnostic. If you can connect your iPod to the car, you can connect Joyride.

Joyride’s $1 million seed round comes Cowboy Ventures and Freestyle Capital as well as from angel investor and Turnable.fm co-founder Seth Goldstein, who is also Joyride’s executive chairman.

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Every year IBM polls its R&D braintrust about what technologies that may have “been at the hairy edge before but are now closer to the scalp,” IBM fellow and VP of innovation Bernie Meyerson told me recently. This year those “closer-to-the-scalp” technologies converge around computers’ growing ability to handle richer, more diverse data and churn out more valuable output — such as the feel of cloth, the smell or taste of food. The general premise is that these sensory and cognitive technologies will convert computers from glorified calculators into true thinking machines.

So, without further ado, here’s IBM’s sixth annual 5 in 5 technology picks.

1: Computers with a sense of touch

Even people who love shopping online say that it’s hard to get a good read on the finished product from a digital image alone. Most of us want to feel the fabric before we buy a big-ticket item. So what if you could sample that cashmere coat from your cell phone before adding it to your shopping cart? Texture data fed into a machine’s piezoelectric drivers can re-create vibrations and temperature on a touch screen can simulate that feel, Meyerson said. “Imagine you have very fine pixels and that each can be heated and vibrated to mimic the sensation of the cloth,” he said.

Some of this capability is available now in rudimentary form in computer games where the controller shakes to indicate an on-screen car collision.

2: Seeing the forest, not just the trees

If you have to rasterize an image in order to analyze it, any sort of correlation will take a long time. If the computer can instead really see and understand that image for what it represents — say, a child, as opposed to a bunch of pixels — it can accelerate the whole process of analysis. That in turn will make the parsing of things like medical images and traffic video much faster. The difference here is between the computer viewing an image and understanding that image without having to break it down into myriad components. That’s the way humans deal with the world. Computers could monitor scanned images of a person over time to watch for and detect changes that indicate a health condition before it gets too serious for example.

3: Hearing the whole story

Just as computers need to see images as whole entities, IBM thinks they also need to hear total sounds — ambient noise, words, music, a lot of inputs to get the full story. “It’s not necessarily just hearing words, hearing is also background noise … if a cell phone caller is in a car with an engine running at 2,000 rpm, you might even be able to tell if the driver is stuck in traffic or moving smoothly,” Meyerson said.

By embedding sensors in flood prone areas, this technology could warn users based on what it’s learned from past sounds, as to whether a mud slide is likely. Computers could also likewise learn based on past experience when a baby’s cry is due to a wet diaper, teething, or something more serious.

4: Digitized taste buds

IBM’s brainiacs think that machines will increasingly be able to taste things — like chocolate or eggplant – and figure out why people do or don’t like that taste. As Kevin Fitchard, GigaOM’s resident foodie, recently reported, some of this is happening now.  For example, researcher and app developer Foodpairing

“has broken down flavor to its molecular components and has compiled databases that can match the flavor of those ingredients against other completely different ingredients. By compiling “foodpairing trees” its technology can identify vegetable or seafood ingredients that reinforce the flavor of different meats, or in some cases, can act as a substitute for a meat entirely.”

This understanding of the chemical elements of food could help people get healthier by subbing in something that tastes like milk chocolate but is better for them.

5: A nose that knows

Breath analysis can do more than keep drunk drivers off the road. What if your smartphone could tell from your breath that you’re about to get a cold? It’s conceivable that your doctor would be able to diagnose you remotely based on that information and prescribe treatment. This technology could also sniff out minuscule amounts of environmental toxins before they hit critical mass, which could have broad public health ramifications.

And then there’s just the quality of life aspect. “You can paint chemical sensors on a surface and when they detect a pattern, they give off a smell — you could make a rich paint with all sorts of sensors that mimic things that you like,” Meyerson said.

So, how’s IBM doing as a sooth sayer?

Since I’m still waiting for the jet packs we were promised decades ago, I’m skeptical about technology predictions, but IBM’s list provides a good starting point to track tech progress and priorities. It’s also fun to grade its prognostication skills.

Looking at last year’s 5 in 5 predictions, it’s fair to say there are hits and misses. For example, last year it said junk mail will get so targeted it will actually cease to be junk at all. If that’s happening, I’m not seeing it.

Another 2011 prediction was we’d get much better at capturing and using wasted kinetic energy  – from people walking, riding bikes,from  running water etc. There is early traction there. Los Angeles is testing advanced flywheel technology as a way to reap wasted energy from braking trains and re-apply it when trains accelerate.  And Pavegen is building sidewalk tiles designed to capture energy from walking pedestrians.

As for mind-reading headsets that measure our brain activity and recognize our facial expressions: Um, no, don’t think so. But to be fair, IBM has 4 more years to make good.

Taking the longer view, looking at IBM’s inaugural list in 2006, it does better. It was on the money with its call that people would be able to access healthcare remotely. There are lots of tele-radiology options and doctors can even perform surgery remotely. IBM also predicted real-time speech translation now exemplified by products like Samsung’s Galaxy speech translation. Meyerson admits to some less successful calls — especially one about hydrogen-powered vehicles — but he’s pretty happy overall with IBM’s effort.

For more check out the video below:

Fabric feature photo courtesy of Shutterstock user sbko; Nose photograph courtesy of  Flickr user Slideshow Bruce

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While great strides have been made in speech recognition over the past decades, the current systems still carry word error rates of 20 percent to 25 percent when handling “arbitrary speech,” Microsoft’s Richard Rashid wrote in a blog post.  (Do you hear that Siri?)

But now, new technology called Deep Neural Networks, which mimics the way the human brain operates, enables much more discriminating speech recognition, according to Rashid, Microsoft’s chief research officer.

Rashid, who demonstrated the technology at a Microsoft conference in Tianjin, China in late October said the process takes text from the subject’s speech, runs it through a translator, first finding the Chinese (I’m assuming Mandarin) equivalents for his words, then rearranges the words in a way that is appropriate to the new language.

In addition, a text-to-speech system uses samples from a native Chinese speaker and from the English speaker’s own voice from pre-recorded English data to model the sound of the speaker’s voice. Watching a video of Rashid’s demo, it’s clear that the technology impressed the audience of Tianjin students. The system is not perfect, Rashid said, but it does cut word error rate by 30 percent.

Microsoft, by the way, last week inked a deal with 21Vianet, a Shanghai-based ISP, to bring both Windows Azure public cloud services and Office 365 to China.

More here about the translation technology from TheNextWeb and SlashGear. And now, check out the video.

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Google has always been a fan of the idea that more data is better, as exemplified by Research Director Peter Norvig’s stance that, generally speaking, more data trumps better algorithms (see, e.g., his 2009 paper titled “The Unreasonable Effectiveness of Data“). Although some hair-splitting does occur about the relative value (or lack thereof) of algorithms in Norvig’s assessment, it’s pretty much an accepted truth at this point and drives much of the discussion around big data. The more data your models have from which to learn, the more accurate they become — even if they weren’t cutting-edge stuff to begin with.

No surprise, then, it turns out that more data is also better for training speech-recognition systems. The researchers found that data sets and larger language models (here’s a Wikipedia explanation of the n-gram type involved in Google’s research) result in fewer errors predicting the next word based on the words that precede it. Discussing the research in a blog post on Wednesday, Google research scientist Ciprian Chelba gives the example that a good model will attribute a higher probability to “pizza” as the next word than to “granola” if the previous two words were “New York.” When it comes to voice search, his team found that “increasing the model size by two orders of magnitude reduces the [word error rate] by 10% relative.”

The real key, however — as any data scientist will tell you — is knowing what type of data is best to train your models, whatever they are. For the voice search tests, the Google researchers used 230 billion words that came from “a random sample of anonymized queries from google.com that did not trigger spelling correction.” However, because people speak and write prose differently than they type searches, the YouTube models were fed data from transcriptions of news broadcasts and large web crawls.

“As far as language modeling is concerned, the variety of topics and speaking styles makes a language model built from a web crawl a very attractive choice,” they write.

This research isn’t necessarily groundbreaking, but helps drive home the reasons that topics such as big data and data science get so much attention these days. As consumers demand ever smarter applications and more frictionless user experiences, every last piece of data and every decision about how to analyze it matters.

Feature image courtesy of Shutterstock user watcharakun.

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Lexee actually builds off Nuance’s technology for online speech recognition and speech to text, so it’s similar in actual performance. The system is designed to help users accomplish tasks and interact using conversational speech. But Angel believes Lexee can stand out because of the ease with which developers can create their conversation flows and the analytical data that they can get back from Lexee.

Lexee uses a simple point-and-click system called SiteBuilder, a toolkit that Angel first developed for IVR and call center applications. It allows non-developers to easily apply voice to any customer interaction. Lexee will also rely on Angel’s analytics, so developers will get back information on how consumers are interacting with Lexee and where any problems might arise in conversation flows.

Angel President Dave Rennyson said neither Nina nor AT&T’s Watson, which also has its own SDK, provides that mix of ease of use and reporting. He said Lexee was used to voice-enable a Salesforce.com application in just four days. Angel, a division of MicroStrategy, currently has about 1,000 enterprise customers using its existing customer experience management tools including Best Buy, Pfizer, Barnes & Noble and Facebook. He said those companies could be interested in re-using some of their work and applying Lexee to their mobile applications.

Lexee is just another sign of how our future will be filled with voice-enabled personal assistants. Siri has opened a lot of people’s eyes as to how speech can be used to get at complex tasks and can be combined with other tools to make us a lot more efficient.

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The service’s vocabulary and contextual understanding is pretty limited. For instance, if you ask Genius to “find a restaurant” it will pull up the nearest eatery on Google Maps. But if you ask it to find “nearby restaurants” it searches Maps for a joint named “Nearby.” If you’re like me, you’ve probably fiddled with the button a few times and never touched it again, despite its relative convenience on the phone’s faceplate.

But T-Mobile has given Genius a much-needed overhaul, at least on the latest versions of the MyTouch manufactured by Huawei. Nuance Communications, which powers the voice-recognition features on T-Mobile devices, is upgrading Genius’ capabilities and the features of the Genius service, providing a deeper level of natural-language understanding and integrating the service with a much broader array of content sources beyond Maps and Google Search.

For instance, if I were to ask the new Genius for nearby restaurants, it would not only understand my intent but also do its searching on Yelp, pulling up dining options nearby and displaying their rankings and reviews. If I were to change that command to “make a reservation at nearby restaurants,” it would bring me to OpenTable’s website and display eateries in the vicinity that accept online bookings.

If this sounds familiar then you’ve probably used Nuance’s consumer semantic-search app, Dragon Go, available for Android and iOS smartphones. In fact, if you look at the long list of 200 content partners the Genius can access, they’re the same used by Dragon Go. Nuance wouldn’t acknowledge specifically that T-Mobile is white-labeling the semantic-search app, but it’s pretty obvious that’s exactly what it’s doing — and it’s by no means a bad thing. I’m a big fan of Nuance’s intuitive little search app, and being able to access it in fewer steps is a bonus.

The official line, though, is that T-Mobile has basically upgraded its relationship with Nuance to a kind of platinum status. The old Genius tapped into Nuance’s basic speech-recognition APIs, but it had none of the rules-based language-parsing abilities of Nuance’s more-sophisticated offerings. By adding greater contextual understanding and a host of content providers, T-Mobile may be able to turn a pretty lame voice-command feature into something quite useful. So far, though, it’s only available on the MyTouch and MyTouch Q.

Both T-Mobile and Nuance will be represented at GigaOM’s Mobilize conference next month. Nuance CTO Vlad Sejnoha will be speaking on a panel about the future smartphone interface on Sept. 20, while T-Mobile SVP of marketing Brad Duea will discuss the evolution of voice services on Sept. 21.

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For Google, it’s all about collecting and analyzing billions of data points to learn what each one really means. With Knowledge Graph, for example, Google uses a “database of more than 500 million real-world people, places and things with 3.5 billion attributes and connections among them.” It’s those connections that are the key, as they’re what make the system smart enough to know what you’re looking for that wouldn’t naturally show up in a standard keyword search.

Although Google hasn’t come out and said so, I’d imagine the Knowledge Graph utilizes Google’s Pregel graph processing engine. Graph processing and databases are catching on in social networks and other large-scale environments because they organize pieces of data by how they’re connected to one another. Those connections are called edges, and they’d keep Knowledge Graph results both informative and focused because the system knows how closely they’re related in any given circumstance.

This example of a personalized interest graph from Gravity Labs illustrates how one might visualize a graph, in this case the connections between a reader’s perceived interests:

Of course, Google has another tool at its disposal, which is the collective wisdom it’s able to glean from billions of searches every day. So, as Singhal wrote when first explaining Knowledge Graph in May, “[W]e can now sometimes help answer your next question before you’ve asked it, because the facts we show are informed by what other people have searched for. For example, the information we show for Tom Cruise answers 37 percent of next queries that people ask about him.”

Google’s other big announcement today is improved voice search on mobile phones, both Android and iOS. Here’s how Singhal describes the new capability:

You just need to tap the microphone icon and ask your question, the same way you’d ask a friend. For example, ask “What movies are playing this weekend?” and you’ll see your words streamed back to you quickly as you speak. Then Google will show you a list of the latest movies in theaters near you, with schedules and even trailers. … When Google can supply a direct answer to your question, you’ll get a spoken response too.

On Monday, a Google Research blog post noted how the company’s work on neural networks — which it famously used to train a system capable of detecting cats and human faces in video streams — is being used to power speech recognition in the Jelly Bean release of Android. Seventeen-year-old Brittany Wenger recently won the Google Science Fair by building an application atop Google App Engine that uses a neural network to help detect breast cancer.

As one might imagine, however, the big challenge for Google, Microsoft , Apple and everyone else trying to provide intelligent but intuitive user experiences is figuring out how to shape high computer science into easily digestible formats on ever-smaller devices. Search would certainly be a more effective tool if everyone could write complex queries directly against a company’s database, but the trick is making products good enough that we don’t have to. It’s boiling years of machine learning, natural-language processing and neural network research into “you ask a question and your phone spits back the right answer.”

Feature image courtesy of Shutterstock user Sebastian Kaulitzki.

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The patents owned by National Cheng Kung University were issued in 2007 and 2010. Siri began life as a U.S. government-funded artificial intelligence project at SRI International, but was spun out as its own company, Siri, in 2007 and commercialized. Apple bought Siri in 2010.

The calculation of the damages the university is seeking “would be based on Apple’s U.S. sales of devices that use Siri,” according to its lawyer. That basically means sales of all iPhone 4S and new iPads with iOS 6 software, which is coming this fall. So the school is looking for a very large chunk of change as compensation.

Apple is involved in dozens of lawsuits regarding its mobile technology, including the very high-profile Samsung trial that kicks off Monday in California. Is the overall climate surrounding handset makers and the mobile industry encouraging more of these kind of lawsuits from international companies? Or, as my colleague Jeff Roberts has argued, has Apple’s decision to settle with the near-bankrupt Proview over the iPad trademark in China last month for $60 million emboldened more potential plaintiffs?

Apple is the only target of the school’s suit for now, but Siri competitors are in the crosshairs as well: the school’s lawyer told Reuters it is also looking at both Microsoft and Google’s speech-to-text products for possible violations of the same two patents.

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