Posts Tagged ‘Qualcomm’
June 8, 2017
Since the beginning, Sensory has been a pioneer in advancing AI technologies for consumer electronics. Not only did Sensory implement the first commercially successful speech recognition chip, but we also were first to bring biometrics to low cost chips, and speech recognition to Bluetooth devices. Perhaps what I am most proud of though, more than a decade ago Sensory introduced its TrulyHandsfree technology and showed the world that wakeup words could really work in real devices, getting around the false accept and false reject, and power consumption issues that had plagued the industry. No longer did speech recognition devices require button presses…and it caught on quickly!
Let me go on boasting because I think Sensory has a few more claims to fame… Do you think Apple developed the first “Hey Siri” wake word? Did Google develop the first “OK Google” wake word? What about “Hey Cortana”? I believe Sensory developed these initial wake words, some as demos and some shipped in real products (like the Motorola MotoX smartphone and certain glasses). Even third-party Alexa and Cortana products today are running Sensory technology to wake up the Alexa cloud service.
Sensory’s roots are in neural nets and machine learning. I know everyone does that today, but it was quite out of favor when Sensory used machine learning to create a neural net speech recognition system in the 1990’s and 2000’s. Today everyone and their brother is doing deep learning (yeah that’s tongue in cheek because my brother is doing it too! (http://www.cs.colorado.edu/~mozer/index.php). And a lot of these deep learning companies are huge multi-billion-dollar business or extremely well-funded startups.
So, can Sensory stay ahead now and continuing pioneering innovation in AI now that everyone is using machine learning and doing AI? Of course, the answer is yes!
Sensory is now doing computer vision with convolutional neural nets. We are coming out with deep learning noise models to improve speech recognition performance and accuracy, and are working on small TTS systems using deep learning approaches that help them sound lifelike. And of course, we have efforts in biometrics and natural language that also use deep learning.
We are starting to combine a lot of technologies together to show that embedded systems can be quite powerful. And because we have been around longer and thought through most of these implementations years before others, we have a nice portfolio of over 3 dozen patents covering these embedded AI implementations. Hand in hand with Sensory’s improvements in AI software, companies like ARM, NVidia, Intel, Qualcomm and others are investing and improving upon neural net chips that can perform parallel processing for specialized AI functions, so the world will continue seeing better and better AI offerings on “the edge”.
Curious about the kind of on-device AI we can create when combining a bunch of our technologies together? So were we! That’s why we created this demo that showcases Sensory’s natural language speech recognition, chatbots, text-to-speech, avatar lip-sync and animation technologies. It’s our goal to integrate biometrics and computer vision into this demo in the months ahead:
Let me know what you think of that! If you are a potential customer and we sign an NDA, we would be happy to send you an APK of this demo so you can try it yourself! For more information about this exciting demo, please check out the formal announcement we made: http://www.prnewswire.com/news-releases/sensory-brings-chatbot-and-avatar-technology-to-consumer-devices-and-apps-300470592.html
June 22, 2016
I’ve written a series of blogs about consumer devices with speech recognition, like Amazon Echo. I mentioned that everyone is getting into the “always listening” game (Alexa, OK Google, Hey Siri, Hi Galaxy, Assistant, Hey Cortana, OK Hound, etc.), and I’ve explained that privacy concerns attempt to be addressed by putting the “always listening” mode on the device, rather than in the cloud.
Let’s now look deeper into the “always listening” approaches and compare some of the different methods and platforms available for embedded triggers.
There are a few basic approaches for running embedded voice wakeup triggers:
First, is running on an embedded DSP, microprocessor, and/or smart microphones. I like to think of this as a “deeply embedded: approach as opposed to running embedded on the operating system (OS). Knowles recently announced a design with a smart mike that provides low-power wake up assistance.
Many leading chip companies have small DSPs that are enabled for “wake up word” detection. These vendors include Audience, Avnera, Cirrus Logic, Conexant, DSPG, Fortemedia, Intel, InvenSense, NXP, Qualcomm, QuickLogic, Realtek, STMicroelectronics, TI, and Yamaha. Many of these companies combine noise suppression or acoustic echo cancellation to make these chips add value beyond speech recognition. Quicklogic recently announced availability of an “always listening” sensor fusion hub, the EOS S3, which lets the sensor listen while consuming very little power.
Next is DSP IP availability. The concept of low-power voice wakeup has gotten so popular amongst processor vendors that the leading DSP/MCU IP cores from ARM, Cadence, CEVA, NXP CoolFlux, Synopsys, and Verisilicon all offer this capability, and some even offer special versions targeting this function.
Running on an embedded OS is another option. Bigger systems like Android, Windows, or Linux can also run voice wake-up triggers. The bigger systems might not be so applicable for battery-operated devices, but they offer the advantage of being able to implement larger and more powerful voice models that can improve accuracy. The DSPs and MCUs might run a 50-kbyte trigger at 1 mA, while bigger systems can cut error rates in half by increasing models to hundreds of megabytes and power consumption to hundreds of milliamps. Apple used this approach in its initial implementation of Siri, thus explaining why the iPhone needed to be plugged in to be “always listening.”
Finally, one can try combinations and multi-level approaches. Some companies are implementing low-power wake-up engines that look to a more powerful system when woken up to confirm its accuracy. This can be done on the device itself or in the cloud. This approach works well for more complex uses of speech technology like speaker verification or identification, where the DSPs are often crippled in performance and a larger system can implement a more state of the art approach. It’s basically getting the accuracy of bigger models and systems, while lowering power consumption by running a less accurate and smaller wakeup system first.
A variant of this approach is accomplished with a low-power speech detection block acting as an always listening front-end, that then wakes up the deeply embedded recognition. Some companies have erred by using traditional speech-detection blocks that work fine for starting a recording of a sentence (like an answering machine), but fail when the job is to recognize a single word, where losing 100 ms can have a huge effect on accuracy. Sensory has developed a very low power hardware sound-detection block that runs on systems like the Knowles mike and Quicklogic sensor hub.
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 3, 2015
When I started Sensory over 20 years ago, I knew how difficult it would be to sell software to cost sensitive consumer electronic OEMs that would know my cost of goods. A chip based method of packaging up the technology made a lot of sense as a turnkey solution that could maintain a floor price by adding the features of a microcontroller or DSP with the added benefit of providing speech I/O. The idea was “buy Sensory’s micro or DSP and get speech I/O thrown in for free”.
After about 10 years it was becoming clear that Sensory’s value add in the market was really in technology development, and particularly in developing technologies that could run on low cost chips and with smaller footprints, less power, and superior accuracy than other solutions. Our strategy of using trailing IC technologies to get the best price point was becoming useless because we lacked the scale to negotiate the best pricing, and more cutting edge technologies were becoming further out of reach; even getting the supply commitments we needed was difficult in a world of continuing flux between over and under capacity.
So Sensory began porting our speech technologies onto other people’s chips. Last year about 10% of our sales came from our internal IC’s! Sensory’s DSP, IP, and platform partners have turned into the most strategic of our partnerships.
Today in the semiconductor industry there is a consolidation that is occurring that somewhat mirrors Sensory’s thinking over the past 10 years, albeit at a much larger scale. Avago pays $37 billion dollars for Broadcom, Intel pays $16.7B for Altera, and NXP pays $12B for Freescale, and the list goes on, dwarfing acquisitions of earlier time periods.
It used to be the multi-billion dollar chip companies gobbled up the smaller fabless companies, but now even the multibillion-dollar chip companies are being gobbled up. There’s a lot of reasons for this but economies of scale is probably #1. As chips get smaller and smaller, there are increasing costs for design tools, tape outs, prototyping, and although the actual variable per chip cost drops, the fixed costs are skyrocketing, making consolidation and scale more attractive.
That sort of consolidation strategy is very much a hardware centered philosophy. I think the real value will come to these chip giants through in house technology differentiation. It’s that differentiation that will add value to their chips, enabling better margins and/or more sales.
I expect that over time the chip giants will realize what Sensory concluded 10 years ago…that machine learning, algorithmic differentiation, and software skills, are where the majority of the value added equation on “smart” chips needs to come from, and that improving the user experience on devices can be a pot of gold! In fact, we have already seen Intel, Qualcomm and many other chip giants investing in speech recognition, biometrics, and other user experience technologies, so the change is underway!
March 3, 2015
It feels like I had a whole week’s worth of the trade show wrapped into one day! By the time mid week hits, I’ll surely be ready to head home! Here are some of the highlights from the first day of Mobile World Congress 2015:
September 4, 2013
Samsung was kind enough to invite me to their roll-out of Galaxy Gear and Galaxy Note 3, but I had no plans to be at IFA Berlin, and I couldn’t justify the time to get out to New York. I did catch some of the roll-out live on my computer…a few misc. thoughts:
August 21, 2013
Saw an article about game changers in the Galaxy Note 3.
It has a few interesting insights. They refer to Samsung’s S-Voice now as “Always on S Voice” and mention that the new Note 3 will be designed to be always on, listening for your wake up command.
The Galaxy Note 3 also uses the Qualcomm SnapDragon 800. This is the chip from Qualcomm that has an always listening wake up command built in. Sorry, Qualcomm, but I don’t think Samsung will be using your technology!
The best performing “always listening” processors combine Sensory’s TrulyHandsfree with an ultra-low power chip, like IP from Tensilica and CEVA. Chip companies like Cirrus Logic, DSPG, Realtek, and Wolfson seem well positioned to lead in mobile chips with “always on” listening features.