DrLAL is correct. The bottom line is small footprint, low resource drain and cost effective for the chip manufacturer. That's why companies prefer a vendor like Rubidium as opposed to one that demands more resources like Nuance.
I tend to agree with DrLAL. You need a lot of acoustic databases to be accommodated on the chip for the parallel searches to come up with meaningful results. If they stick to the automotive speech recognition domain they can probably get away with a limited vocabulary but they will need pretty fast search capabilities for real-time response in a vehicle setting. Wonder if the Apple folks have not thought to embed their Siri in cars' infotainment system. Seems like a natural.
"Spansion promises its accelerators will cut in half both system response time and the CPU workload for voice recognition. The chip essentially stores acoustic databases and performs parallel searches across them". Won't it run out of storage space?
With a lot of car makers coming to the Valley to do R&D of infotainment, voice recognition seems like a good move. Voice recognition seems to be the MMI in the future. Samsung SmartTV supports it. The technology may likely apply everywhere.
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. Are the design challenges the same as with embedded systems, but with a little developer- and IT-skills added in? What do engineers need to know? Rick Merritt talks with two experts about the tools and best options for designing IoT devices in 2016. Specifically the guests will discuss sensors, security, and lessons from IoT deployments.