Archive for the ‘Voice Control’ Category
August 22, 2016
Sensory is proud to announce that it has been awarded with two 2016 Speech Tech Magazine Awards. With some stiff competition in the speech industry, Sensory continues to excel in offering the industry’s most advanced embedded speech recognition and speech-based security solutions for today’s voice-enabled consumer electronics movement.
The 2016 Speech Technology Awards include:
Speech Luminary Award – Awarded to Sensory’s CEO, Todd Mozer
“What really impresses me about Todd is his long commitment to speech technology, and specifically, his focus on embedded and small-footprint speech recognition,” says Deborah Dahl, principal at Conversational Technologies and chair of the World Wide Web Consortium’s Multimodal Interactions Working Group. “He focuses on what he does best and excels at that.”
Star Performers Award – Awarded to Sensory for its contributions in enabling voice-enabled IoT products via embedded technologies
“Sensory has always been in the forefront of embedded speech recognition, with its TrulyHandsfree product, a fast, accurate, and small-footprint speech recognition system. Its newer product, TrulyNatural, is ground- breaking because it supports large vocabulary speech recognition and natural language understanding on embedded devices, removing the dependence on the cloud,” said Deborah Dahl, principal at Conversational Technologies and chair of the World Wide Web Consortium’s Multimodal Interactions Working Group. “While cloud-based recognition is the right solution for many applications, if the application must work regardless of connectivity, embedded technology is required. The availability of TrulyNatural embedded natural language understanding should make many new types of applications possible.”
– Guest Blog by Michael Farino
June 17, 2016
Hey Siri, Cortana, Google, Assistant, Alexa, BlueGenie, Hound, Galaxy, Ivee, Samantha, Jarvis, or any other voice-recognition assistant out there.
Now that Google and Apple have announced that they’ll be following Amazon into the home far-field voice assistant business, I’m wondering how many things in my home will always be on, listening for voice wakeup phrases. In addition, how will they work together (if at all). Let’s look at some possible alternatives:
Co-existence. We’re heading down a path where we as consumers will have multiple devices on and listening in our homes and each device will respond to its name when spoken to. This works well with my family; we just talk to each other, and if we need to, we use each other’s names to differentiate. I can have friends and family over or even a big party, and it doesn’t become problematic calling different people by different names.
The issue for household computer assistants all being on simultaneously is that false fires will grow in direct proportion to the number of devices on and listening. With Amazon’s Echo, I get a false fire about every other day, and Alexa does a great job of listening to what I say after the false fire and ignoring if it doesn’t seem to be an intended command. It’s actually the best performing system I’ve used and the fact that its starts playing music or talking every other week is a testament to what a good job they have done. However, interrupting my family every other week is not good enough. And if I have five always-listening devices interrupting us 10 times a month, that becomes unacceptable. And if they don’t do as good a job as Alexa, and interrupt more frequently, it becomes quite problematic.
Functional winners. Maybe each device could own a functional category. For example, all my music systems could use Alexa, my TV’s use Hi Galaxy, and all appliances are Bosch. Then I’d have less “names” to call out to and there would be some big benefits: 1) The devices using the same trigger phrase could communicate and compare what they heard to improve performance; 2) More relevant data could be collected on the specific usage models, thus further improving performance; and 3) With less names to call out, I’d have fewer false fires. Of course, this would force me as a consumer to decide on certain brands to stick to in certain categories.
Winner take all. Amazon is adopting a multi-pronged strategy of developing its own products (Echo, Dot, Tap, etc.) and also letting its products control other products. In addition, Amazon is offering the backend Alexa voice service to independent product developers. It’s unclear whether competitors will follow suit, but one thing is clear—the big guys want to own the home, not share it.
Amazon has a nice lead as it gets other products to be controlled by Echo. The company even launched an investment fund to spur more startups writing to Alexa. Consumers might choose an assistant we like (and we think performs well) and just stick with that across the household. The more we share with that assistant, the better it knows us, and the better it serves us. This knowledge base could carry across products and make our lives easier.
Just Talk. In the “co-existence” case previously mentioned, there six people in my household, so it can be a busy place. But when I speak to someone, I don’t always start with their name. In fact, I usually don’t. If there’s just one other person in the room, it’s obvious who I’m speaking to. If there are multiple people in the room, I tend to look at or gesture toward the person I’m addressing. This is more natural than speaking their name.
An “always listening” device should have other sensors to know things like how many people are in the room, where they’re standing and looking at, how they’re gesturing, and so on. These are the subconscious cues humans use to know who is talking to us, and our devices would be smarter and more capable if they could do it.
June 15, 2016
“Credit to the team at Amazon for creating a lot of excitement in this space,” Google CEO Sundar Pichai. He made this comment during his Google I/O speech last week when introducing Google’s new voice-controlled home speaker, Google Home which offers a similar sounding description to Amazon’s Echo. Many interpreted this as a “thanks for getting it started, now we’ll take over,” kind of comment.
Google has always been somewhat marketing challenged in naming its voice assistant. Everyone knows Apple has Siri, Microsoft has Cortana, and Amazon has Alexa. But what is Google’s voice assistant called? Is it Google Voice, Google Now, OK Google, Voice Actions? Even those of us in the speech industry have found Google’s branding to be confusing. Maybe they’re clearing that up now by calling their assistant “Google Assistant.” Maybe that’s the Google way of admitting it’s an assistant without admitting they were wrong by not giving it a human sounding name.
The combination of the early announcement of Google Home and Google Assistant has caused some to comment that Amazon has BIG competition at best, and at worst, Amazon’s Alexa is in BIG trouble.
I thought I’d point out a few good reasons why Amazon is in pretty good shape:
Of course, Amazon has its challenges as well, but I’ll leave that for another blog.
May 6, 2016
Rich Nass and Barbara Quinlan from Open Systems Media visited Sensory on their “IoT Roadshow”.
IoT is a very interesting area. About 10 years ago we saw voice controlled IoT on the way, and we started calling the market SCIDs – Speech Controlled Internet Devices. I like IoT better, it’s certainly a more popular name for the segment! ;-)
I started our meeting off by talking about Sensory’s three products – TrulyHandsfree Voice Control, TrulySecure Authentication, and TrulyNatural large vocabulary embedded speech recognition.
Although TrulyHandsfree is best known for its “always on” capabilities, ideal for listening for key phrases (like OK Google, Hey Cortana, and Alexa), it can be used a ton of other ways. One of them is for hands-free photo taking, so no selfie stick is required. To demonstrate, I put my camera on the table and took pictures of Barbara and Rich. (Normally I might have joined the pictures, but their healthy hair, naturally good looks, and formal attire was too outclassing for my participation).
There’s a lot of hype about IoT and Wearables and I’m a big believer in both. That said, I think Amazon’s Echo is the perfect example of a revolutionary product that showcases the use of speech recognition in the IoT space and am looking forward to some innovative uses of speech in Wearables!
Here’s the article they wrote on their visit to Sensory and an impromptu video showing TrulyNatural performing on-device navigation, as well as a demo of TrulySecure via our AppLock Face/Voice Recognition app.
Rich Nass, Embedded Computing Brand Director
If you’re an IoT device that requires hands-free operation, check out Sensory, just like I did while I was OpenSystems Media’s IoT Roadshow. Sensory’s technology worked flawlessly running through the demo, as you can see in the video. We ran through two different products, one for input and one for security.
October 1, 2015
Todd Mozer’s interview with Martin Wasserman on FutureTalk
August 6, 2015
We first came out with TrulyHandsfree about five years ago. I remember talking to speech tech executives at MobileVoice as well as other industry tradeshows, and when talking about always-on hands-free voice control, everybody said it couldn’t be done. Many had attempted it, but their offerings suffered from too many false fires, or not working in noise, or consuming too much power to be always listening. Seems that everyone thought a button was necessary to be usable!
In fact, I remember the irony of being on an automotive panel, and giving a presentation about how we’ve eliminated the need for a trigger button, while the guy from Microsoft presented on the same panel the importance of where to put the trigger button in the car.
Now, five years later, voice activation is the norm… we see it all over the place with OK Google, Hey Siri, Hey Cortana, Alexa, Hey Jibo, and of course if you’ve been watching Sensory’s demos over the years, Hello BlueGenie!
Sensory pioneered the button free, touch free, always-on voice trigger approach with TrulyHandsfree 1.0 using a unique, patented keyword spotting technology we developed in-house– and from its inception, it was highly robust to noise and it was ultra-low power. Over the years we have ported it to dozens of platforms, Including DSP/MCU IP cores from ARM, Cadence, CEVA, NXP CoolFlux, Synopsys and Verisilicon, as well as for integrated circuits from Audience, Avnera, Cirrus Logic, Conexant, DSPG, Fortemedia, Intel, Invensense, NXP, Qualcomm, QuickLogic, Realtek, STMicroelectronics, TI and Yamaha.
This vast platform compatibility has allowed us to work with numerous OEMs to ship TrulyHandsfree in over a billion products!
Sensory didn’t just innovate a novel keyword spotting approach, we’ve continually improved it by adding features like speaker verification and user defined triggers. Working with partners, we lowered the draw on the battery to less than 1mA, and Sensory introduced hardware and software IP to enable ultra-low-power voice wakeup of TrulyHandsfree. All the while, our accuracy has remained the best in the industry for voice wakeup.
We believe the bigger, more capable companies trying to make voice triggers have been forced to use deep learning speech techniques to try and catch up with Sensory in the accuracy department. They have yet to catch up, but they have grown their products to a very usable accuracy level, through deep learning, but lost much of the advantages of small footprint and low power in the process.
Sensory has been architecting solutions for neural nets in consumer electronics since we opened the doors more than 20 years ago. With TrulyHandsfree 4.0 we are applying deep learning to improve accuracy even further, pushing the technology even more ahead of all other approaches, yet enabling an architecture that has the ability to remain small and ultra-low power. We are enabling new feature extraction approaches, as well as improved training in reverb and echo. The end result is a 60-80% boost in what was already considered industry-leading accuracy.
I can’t wait for TrulyHandsfree 5.0…we have been working on it in parallel with 4.0, and although it’s still a long ways off, I am confident we will make the same massive improvements in speaker verification with 5.0 that we are doing for speech recognition in 4.0! Once again further advancing the state of the art in embedded speech technologies!
June 11, 2015
Guest post by: Michael Farino
Sensory’s CEO, Todd Mozer joined Alan Taylor, host of Popular Science Radio, in a fun discussion about artificial intelligence, Sensory’s involvement with the Jibo robot development team, and also gave the show’s listeners a look into the past 20 years of speech recognition. Todd and Alan additionally discussed some of the latest advancements in speech technology, and Todd provided an update on Sensory’s most recent achievements in the field of speech recognition as well as a brief look into what the future holds.
Listen to the full radio show at the link below:
Big Bang Theory, Science, and Robots | FULL EPISODE | Popular Science Radio #269
May 1, 2015
Winning on Accuracy & Speed… How can a tiny player like Sensory compete in deep learning technology with giants like Microsoft, Google, Facebook, Baidu and others?
There’s a number of ways, and let me address them specifically:
These 3 items together have provided Sensory with the highest quality embedded speech engines in the world. It’s worth reiterating why embedded is needed, even if speech recognition can all be done in the cloud:
April 22, 2015
How does Big Data and Privacy fit into the whole Deep Learning Puzzle?
Privacy and Big Data have become big concerns in the world of Deep Learning. However, there is an interesting relationship between the Privacy of personal data and information, Big Data, and Deep Learning. That’s because a lot of the Big Data is personal information used as the data source for Deep Learning. That’s right, to make vision, speech and other systems better, many companies invade users’ personal information and the acquired data is used to train their neural networks. So basically, Deep Learning is neural nets learning from your personal data, stats, and usage information. This is why when you sign a EULA (end user license agreement) you typically give up the rights to your data, whether its usage data, voice data, image data, personal demographic info, or other data supplied through the “free” software or service.
Recently, it was brought to consumers’ attention that some TVs and even children’s toys were listening in on consumers, and/or sharing and storing that information to the cloud. A few editors called me to get my input and I explained that there are a few possible reasons for devices to do this kind of “spying” and none of which are the least bit nefarious: The two most common reasons are 1) The speech recognition technology being used needs the voice data to train better models, so it gets sent to the cloud to be stored and used for Deep Learning and/or 2) The speech recognition needs to process the voice data in the cloud because it is unable to do so on the device. (Sensory will change this second point with our upcoming TrulyNatural release!)
The first reason is exactly what I’ve been blogging about when we say Deep Learning. More data is better! The more data that gets collected, the better the Deep Learning can be. The benefits can be applied across all users, and as long as the data is well protected and not released, then it only has beneficial consequences.
Therein lies the challenge: “as long as the data is well protected and not released…” If banks, billion dollar companies and governments can’t protect personal data in the cloud, then who can, and why should people ever assume their data is safe, especially from systems where there is no EULA is place and data is being collected without consent (which happens all the time BTW)?
Having devices listen in on people and share their voice data with the cloud for Deep Learning or speech recognition processing is an invasion of privacy. If we could just keep all of the deep neural net and recognition processing on device, then there would be no need to risk the security of peoples’ personal data by sharing and storing it on the cloud… and its with this philosophy that Sensory pioneered an entirely different, “embedded” approach to deep neural net based speech recognition which we will soon be bringing to market. Sensory actually uses Deep Learning approaches to train our nets with data collected from EULA consenting and often paid subjects. We then take the recognizer built from that research and run it on our OEM customers’ devices and because of that, never have to collect personal data; so, the consumers who buy products from Sensory’s OEM customers can rest assured that Sensory is never putting their personal data at risk!
In my next blog, I’ll address the question about how accurate Sensory can be using deep nets on device without continuing data collection in the cloud. There are actually a lot of advantages for running on device beyond privacy, and it can include not only response time but accuracy as well!
April 15, 2015
Deep Neural Nets, Deep Belief Nets, Deep Learning, DeepMind, DeepFace, DeepSpeech, DeepImage… Deep is all the rage! In my next few blogs I will try to address some of the questions and issues surrounding all of these “deep” thoughts including:
Part 1: What is Deep Learning and is Sensory Just Jumping on the Bandwagon?
Artificial Neural Network approaches have been around for a long time, and have gone in and out of favor. Neural Nets are an approach within the field of Machine Learning and today they are all the rage. Sensory has been working with Neural Net technology since our founding more than 20 years ago, so the approach is certainly not new for us. We are not just jumping on the bandwagon… we are one of the leading carts! ;-)
Neural Networks are very loosely modeled after how our brains work – nonlinear, parallel processing, and learning from exposure to data rather than being programmed. Unlike common computer architectures that separate memory from processing, our brains have billions of neurons that communicate and process all in parallel and with huge quantities of connections. This architecture based on how our brains work turns out to be much better than traditional computer programs at dealing with ambiguous and “sensory” information like vision and speech – a little Trivia: that’s how we came up with the name Sensory!
In the early days of Sensory, we were often asked by engineers, “What kind of neural networks are you running?” They were looking for a simple answer, something like a “Kohonen Net.” I once asked my brother, Mike Mozer, a pioneer in the field of neural nets, a Sensory co-founder, and a professor of computer science at U. Colorado Boulder, for a few one liners to satisfy curious engineers without giving anything away. We had two lines: the first being, “a feed forward multi-layer net” which satisfied 90% of those asking, and the other response for those that asked for more was, “it’s actually a nonlinear and multivariate function.” That quieted pretty much everyone down.
In the last five years Neural Networks have proven to be the best-known approaches for various recognition and ambiguous data challenges like vision and speech. The breakthrough and improvement in performance came from these various terms that use the word “deep.” The “deep” approaches entailed more complex architectures that receive more data. The architecture relates to the ways that information is shared and processed (like all those connections in our brain), and the increased data allows the system to adapt and improve through continuous learning, hence the terms, “Deep Learning” and “Deep Learning Net.” Performance has improved dramatically in the past five years and Deep Learning approaches have far exceeded traditional “expert-based” techniques for programming complex feature extraction and analysis.