Australian Computer Scientists Develop Digital Face to Add Emotion to Synthesized Speech
How it works is, the user types a phrase for Zoe to say. Six sliders allow you to set the emotions; for example, you could combine happiness and anger, setting them to halfway or full strength, depending on what you want her to convey. Then you can slow down or speed up her speech, giving a pretty large array of tone.
When tested with a group of 20 volunteers, they were able to accurately guess the emotion 77 per cent of the time — more than with the real-life Zoë, with whom the success rate was 73 per cent.
The team sees Zoe being used in the future as a personal assistant, but there are other potential applications as well, because the framework for the face is very light — tens of MBs — which means that it can be incorporated into small devices. It could also enable people to upload their own faces and voices into the program; the team envisions these being used as sort of “face messages” rather than text messages.
(via Virtual talking head can express human emotions - Crave)
Malaysian Computer Detects Emotions, Reads Lips
Scientists in Malaysia are teaching a computer to interpret human emotions based on lip patterns.
The system could improve the way we interact with computers and perhaps allow disabled people to use computer-based communications devices, such as voice synthesizers, more effectively and more efficiently, says Karthigayan Muthukaruppan of Manipal International University.
The system uses a genetic algorithm that gets better and better with each iteration to match irregular ellipse-fitting equations to the shape of a human mouth displaying different emotions.
They have used photos of individuals from Southeast Asia and Japan to train a computer to recognize the six commonly accepted human emotions — happiness, sadness, fear, angry, disgust, surprise — and a neutral expression. The upper and lower lip is each analyzed as two separate ellipses by the algorithm.
The team’s algorithm can successfully classify the seven emotions and a neutral expression described, the scientists say.
(via Computer learning to read lips to detect emotions | KurzweilAI)
User Interface from Neon Genesis Evangelion as GIFs
Great imaginary HCI
(ht samhumphries, ht tohaheavyindustries)
Microsoft Demos Computer Interface That Takes Input From Muscle Movements
This new technology relies on electromyography (EMG), which is basically the muscular equivalent of an EEG — electroencephalography, the basis for brain-computer interfaces. An EMG detects the electrical charges created by muscle cells after they receive a signal from the brain. By placing an armband around the muscles in your forearm — the muscles that control your finger movements — an EMG can accurately detect the movement of your fingers. With training, software can then convert specific EMG readings into gestures, which can be fed into the computer as normal keyboard, mouse, or touchscreen inputs.
(via Microsoft demos muscle-computer interface, air Guitar Hero now a reality | ExtremeTech)
Japanese Telepresence (“Telexistence”) Avatar Transmits Touch, Temperature and Vibration to Remote Operator
The TELESAR V’s hands and fingers are equipped with a number of sensors to capture and relay tactile information to its operator through special gloves. The primary sensor inside each fingertip is a vision-based force sensor which is comprised of a wide-angle camera that looks through a gel-layer mixed with thermochromic ink. When the gel compresses, the thermochromic ink becomes denser, which the camera interprets as force information.
Microphones underneath the robot’s fingertips convert low to mid level vibrations; when pouring marbles from one cup to another (as the robot), the operator feels the tactile sensation from doing so. Furthermore, the operator is able to sense changes in temperature at the robot’s fingertips, thanks to thermoelectric peltier devices which reproduce warm and cold temperature inside the operator’s gloves. Now even an object’s texture can be relayed to the operator.
(via TELESAR V Avatar Transfers Touch, Vibration, Temperature)
Carnegie Mellon Tracks Inventory Using Robot
Great example of the trend toward developing robots that are optimized to work with humans. Humans remain the primary, high-touch interface in the retail environment. The robot performs simple, repetitive tasks, and creates data artifacts (the map and inventory) that are usable for both bots and humans.
While making its rounds, the robot uses a combination of image-processing and machine-learning algorithms; a database of 3-D and 2-D images showing the store’s stock; and a basic map of the store’s layout—for example, where the T-shirts are stacked, and where the mugs live.
The robot has proximity sensors so that it doesn’t run into anything. None of the technologies it uses are new in themselves, says Narasimhan. It’s the combination of different types of algorithms running on a low-power system that makes the system unique.
The map generated by the robot is sent to a large touch-screen system in the store and a real-time inventory list is sent to iPad-carrying staff.
The robot uses a few different tricks to identify items. It looks for barcodes and text; and uses information about the shape, size, and color of an object to determine its identity. These are all pretty conventional computer-vision tasks, says Narasimhan. But the robot also identifies objects based on information about the structure of the store, and items belong next to each other. “If an unidentified bright orange box is near Clorox bleach, it will infer that the box is Tide detergent,” she says.
Narasimhan’s group developed the system after interviewing retailers about their needs. Stores lose money when they run low on a popular item, and when a customer puts down a jar of salsa in the detergent aisle where it won’t be found by someone who wants to buy it; or when customers ask where something is and clerks don’t know.
So far, the robotic inventory system seems to have helped increase the staff’s knowledge of where everything is. By the fall, Narasimhan expects to learn whether it has also saved the store money.
Chat Bot Wins Turing Test By Simulating an Individual
Unlike several of Eugene’s rivals, which put together sentences by imitating people they have spoken to before or by searching through Twitter transcripts for conversational ideas, Veselov has given his bot a consistent and specific personality. “He has created very much a person where Cleverbot is everybody,” says Carpenter.
Eugene’s character is that of a 13-year-old boy living in Odessa, Ukraine. He has a pet guinea pig and a father who is a gynaecologist. Is 13 about the right age for a chatbot, then? “Thirteen years old is not too old to know everything and not too young to know nothing,” explains Veselov.
A veteran of the Loebner prize and the Chatterbox challenge , Eugene was due a win. “We took second place several times but never were we the winners,” says Veselov.
Did having a personality give him an advantage? “I think any appearance of a particular personality is likely to have a persuasive effect on judges,” says John Barnden, an AI researcher specialising in machine understanding of metaphor at the University of Birmingham, UK, and a fellow judge.
He cautions against concluding that this was Eugene’s edge, however - for that you would have to compare two versions of the same bot, but in one case with personality suppressed.
Teaching Robots to Learn Language Like Children
The team told volunteers, who were varied in age, occupation, gender, experience with children and familiarity with computers, to talk to DeeChee exactly how they would if they wanted to teach a real child the words for colors and patterns.
DeeChee, in turn, was programmed to hear the teacher’s speech as small units of speech called phenomes, not syllables. To DeeChee, the phrase “a red box” might contain any of the following phenomes: a, ar, re, red, e, ed, bo, box, o, ox.
By listening for and responding to praise in response to its babble, DeeChee attempted to piece together what words the teachers were trying to get it to say.
What was interesting about these experiments was not only whether or not DeeChee would succeed at learning words, but also how the volunteers themselves varied in their abilities.
“We wanted to explore human-robot interaction and were deliberately not prescriptive,” explain the authors. “However, leaving participants to talk naturally opened up possibilities of a wide range of behaviour, possibilities that were certainly realized.”
Like in a real teaching setting, the teachers – and DeeChee’s learning – varied. “Some participants were better teachers than others: some of the less good produced very sparse utterances, while other talkative participants praised DeeChee whatever it did, which skewed the learning process towards non-words.”
Overall, though, DeeChee learned. As you can see in the video, the robo-baby was able to pick up simple, one-syllable words like red, green, and heart. DeeChee’s success suggests that similar mechanisms may explain how human babies learn to talk.
(via AI Takes Baby Steps: RoboBaby Learns Words | Science Sushi, Scientific American Blog Network)
RoboCar HV: a Driverless Car That Can Be Driven With an iPad
The Robocar HV being developed by ZMP is a test vehicle for research and development that acquires vehicle sensor data via the vehicle’s controller area network (CAN) to enable vehicle control through an independent controller. The Robocar HV can acquire various information such as speed, engine RPM, number of steering wheel turns, and orientation, and it can control steering, acceleration, and braking. Acquired driving data can also be saved and put into a database in a server via the Internet using a cloud service jointly developed with Microsoft Japan.
(via diginfo ht futurescope)
Putting a Kinect in Every Room to Help You Remember Where You Put Stuff…
What could possibly go wrong?
“We want to make Google for your home,” says Shahriar Nirjon, a computer scientist at the University of Virginia in Charlottesville.
To do this, Nirjon and colleague John Stankovic developed Kinsight, which records the location of household items using a Kinect depth camera in each room. It works by tracking people and detecting the size and shape of any objects they interact with.
Each object is compared to Kinsight’s database for the house and either recognised or added to the list. By following the location of objects over time, Kinsight can even distinguish between two identical-looking things - if it records a mug that seems to have jumped from the living room to the kitchen without passing through the space between, for example, it knows it is likely to be two mugs. The system can locate fist-sized objects with an accuracy of 13 centimetres.
(via Kinect system keeps track of household objects - tech - 07 June 2012 - New Scientist)
