Posts Tagged ‘Avnera’
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!