I have to say, it's very interesting. Right now, I think MeMeMe Mobile (www.memememobile.com) is a new entrant into this vast new field, and it's pretty promising. Come check it out for a few minutes, or sign up under the development track.
Sverrir, as impressive as Conexant's SSP research appears to be, I'm afraid that this is not going to cut it in the end. The market doesn't want noise-tolerant speech recognition systems that make preprogrammed assumptions about the environment in which they are used. The market wants speech technologies that are as noise-tolerant and versatile as the human brain or better. Personally, I want to be able to talk to my car even when I'm standing next to it. I want to tell it to open, close or lock the doors, any door; or open the trunk. Any company that can deliver this technology will make a killing.
It has been obvious for some time that the human brain does not use anything like Bayesian statistics (HMM) to process sounds or anything else. This is the fundamental problem with machine recognition. What is needed is a revolution in our understanding of how humans and animals recognize sounds. Everybody in AI has jumped on the Bayesian bandwagon just as they once jumped on the symbolic bandwagon in the 1950s only to be proven wrong half a century later.
In spite of its current success, the hidden Markov model is not it. SR researchers need to start thinking outside the box in my opinion. I say, get off the bandwagon because it's going nowhere. There's a better way, the correct way, and, as we all know, there's a fabulous prize waiting at the end of the road.
What are the engineering and design challenges in creating successful IoT devices? These devices are usually small, resource-constrained electronics designed to sense, collect, send, and/or interpret data. Some of the devices need to be smart enough to act upon data in real time, 24/7. Specifically the guests will discuss sensors, security, and lessons from IoT deployments.