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Speech Recognition in Silicon
Ben Sullivan writes "NSF-funded researchers are working to develop a silicon-based approach to speech recognition. "The goal is to create a radically new and efficient silicon chip architecture that only does speech recognition, but does this 100 to 1,000 times more efficiently than a conventional computer." Good use of $1 million?"
If this really is true what they're saying, and knowing how much money is invested in speech recognition research on a yearl y basis, yeah, i would definately say that this is one million dollars of great investment...
- Leon Mergen
http://www.solatis.com
Good use of $1 million?
Let me think for a moment... Hell yeah! If we had low power speech processors, the possibilities would be endless. For one, we'd finally have a Star Trek(TM) interface for our homes!
"Computer, lights!"
"Computer, make coffee!"
"Computer, Earl Grey, hot!"
As silly as it may sound, such an interface would be far more efficient than mashing buttons.
In addition, blind people could be significantly helped by this. Many of them already use speech recognition and synthesis to assist in computer usage. Imagine if their computers could suddenly understand them a thousand times better? They could talk to their computers a bit more naturally, thus saving their vocal chords from undue stress.
Other applications (off the top of my head) are:
- Voice notes on embedded devices (store only text!)
- Helpful Kiosks that can give you directions
- A new use for natural language database queries (i.e. Ask the computer what last quarter's net sales were.)
- Voice controlled robots ("You missed a corner, vacuum cleaner")
- Data search by voice ("Find me a channel that plays Star Trek")
Any other cool ideas out there?
Javascript + Nintendo DSi = DSiCade
Carnegie Mellon University's Rob A. Rutenbar is leading a national research team to develop a new, efficient silicon chip that may revolutionize the way humans communicate and have a significant impact on America's homeland security. Rutenbar, a professor of electrical and computer engineering at Carnegie Mellon, working jointly with researchers at the University of California at Berkeley received a $1 million grant from the National Science Foundation to move automatic speech recognition from software into hardware. ''I can ask my cell phone to 'Call Mom,''' says Rutenbar, ''but I can't dictate a detailed email complaint to my travel agent or navigate a complicated Internet database by voice alone.''
From Carnegie Mellon University:
Carnegie Mellon engineering researchers to create speech recognition in silicon
Team to develop new silicon chip
Carnegie Mellon University's Rob A. Rutenbar is leading a national research team to develop a new, efficient silicon chip that may revolutionize the way humans communicate and have a significant impact on America's homeland security.
Rutenbar, a professor of electrical and computer engineering at Carnegie Mellon, working jointly with researchers at the University of California at Berkeley received a $1 million grant from the National Science Foundation to move automatic speech recognition from software into hardware.
''I can ask my cell phone to 'Call Mom,''' says Rutenbar, ''but I can't dictate a detailed email complaint to my travel agent or navigate a complicated Internet database by voice alone.''
The problem is power--or rather, the lack of it. It takes a very powerful desktop computer to recognize arbitrary speech. ''But we can't put a PentiumTM in my cell phone, or in a soldier's helmet, or under a rock in a desert,'' explains Rutenbar, ''the batteries wouldn't last 10 minutes.''
Thus, the goal is to create a radically new and efficient silicon chip architecture that only does speech recognition, but does this 100 to 1,000 times more efficiently than a conventional computer.
The research team is uniquely poised to deliver on this ambitious project. Carnegie Mellon researchers pioneered much of today's successful speech recognition technology. This includes the influential 'Sphinx' project, the basis for many of today's commercial speech recognizers.
''We're still not even close to having a voice interface that will let you throw away your keyboard and mouse, but this current research could help us see speech as the primary modality on cell phones and PDAs,'' said Richard Stern, a professor in electrical and computer engineering and the team's senior speech recognition expert. ''To really throw away the keyboard, we have to go to silicon.'' But enhanced conversations between people and consumer products is not the main goal. ''Homeland security applications are the big reason we were chosen for this award,'' says Rutenbar. ''Imagine if an emergency responder could query a critical online database with voice alone, without returning to a vehicle, in a noisy and dangerous environment. The possibilities are endless.''
Researchers plan to unveil speech-recognition chip architecture in two to three years.
I can just see the anonymous cowards shouting first post at their pcs now
Cruise TT
My friend and I were talking about this. In countries that are more totalitarian, it could be used to root out "dangerous people" www.geocities.com/James_Sager_PA
God spoke to me.
100 to 1000 times more efficient worth $1M? meh. maybe.
100 to 1000 times more accurate worth $1M? definitely.
Damned straight it is! In government terms, that's a pittance. In government-funded science terms, it's downright INFINITESIMAL. It isn't even couch change, it's more like the stale pretzel under the couch cushion.
But, of course, cue the armchair blogging fanatics without a formal science education, waxing poetic about the infinite power and glory of x86 hardware running clever open source software. Maybe we could do it in perl!
I'm curious to see if their research will improve Natural Language Queries, as opposed to just improving speech recognition. There is an important difference between having to say: SELECT name FROM users WHERE id=12345 and saying: Pull up the name of employee number 12345.
-dave
http://millionnumbers.com/ - own the number of your dreams
I once did a lot of work with speech recognition software, having a former significant other who was disabled. I tested a number of programs, and found the biggest problem to be the wide variances in users' dialects. The programs all have to be trained initially to recognize a single users' voice. This means that a program trained for a Bostonian may not work for someone from Arkansas, Texas, or Louisiana. Also, the programs' effectiveness decreased over time if you did not use it regularly.
I don't know how possible it will be to make a program that can recognize all English users. Will someone who speaks Oxford English be recognized as well as a surfer from California? I doubt it.
Never look down your nose at others. Someday, someone is bound to see your boogers.
This seems like a situation where a hardware accelerated approach is pretty sensible. I'm guessing there is large amounts of signal processing involved in speech recognition. With a custom chip like this it probably helps greatly to offload some of that onto a dedicated chip in the same way as GPUs are used on graphics cards. The only problem I can see is that there might not be much market for it. GPUs have an obvious market (games), but there is less demand for speech processing. Star-Trek style interfaces are nice to dream of but for most common tasks a keyboard and mouse will probably give you a faster and more accurate interface.
gmail invite
And imagine how much embarassment could be saved alone by correcting idiotic mispellings of simple words like "speech".
Depends. It's not as good as using it to prevent the deaths of thousands - possibly tens of thousands - of people by ensuring they have clean drinking water and shelter from the elements. But hey - you can't put a price on being able to speak to a computer rather than type when you're ordering a pizza.
During 1994 upto 1998 I did marketign and technical support for IBM's Voicetype Dictation products..
Initially, doing anythign beyond understanding a few words would take special hardware, but after a bit of 'training' highly acurate and fast speech to text was quite a possibility with a specially developed dsp.
Then, the pentium class cpus came about, and a p90 could just do the whole thing without the dsp.
So, now someone is developing a new dedicated piece of silicon for this.. lets see how long it takes for general purpose computers to catch up.
The issue is not that this is not usefull, but that it either has to keep developing, or offer a somewhat longer lasting price/performance ratio or much better features for a logn time to come.
Using specialised DSPs makes more sense to me than burning up generic CPU cycles. There have been many examples over the years of how a specialized DSP is more efficient and effective for a narrow task than a regular CPU. Look at portable MP3 players. They use tiny specialized DSPs to decode the files in a manner that is much more efficient than using a regular CPU.
We'll still need to do traditional development to interpret the data from the DSPs. We'll need to parse the output so that we can use natural commands to control devices.
"Coffee maker, brew 10 cups, strong."
"Bathroom lights, on."
Without some manner of AI to interpret them, these phrases will be useless.
LK
"Hi. This is my friend, Jack Shit, and you don't know him." - Lord Kano
From the blog: ''Homeland security applications are the big reason we were chosen for this award,'' says Rutenbar. ''Imagine if an emergency responder could query a critical online database with voice alone, without returning to a vehicle, in a noisy and dangerous environment. The possibilities are endless.''
Like some slight tweaking in order to deploy massive voiceprint-recognition silicon arrays for amazingly efficient automatic realtime conversation transcription and identity determination, attached to Echelon.
So cool... so potentially evil... head begins to hurt... tinfoil hat burning....
Although $1million significantly can speed things up, this is a pretty ambitious undertaking.
My Master's research was on implementing machine learning in hardware, specifically support vector machines.
Now, they have much more money than I did, and probably this will be a collaboration involving many graduate students, but converting complex algorithms from software to hardware is no easy task.
It is just easier to do things in software, that's why it has evolved. The modular layers of abstraction allow a Computer Scientist working in machine learning or speech recognition to not have to worry about how the underlying hardware works.
Working in hardware, a lot these issues come face to face. Particularly since you want an architecture on a chip, whereas in a conventional desktop/server system there are resources such as lots of RAM, harddrive space, etc are available and their interconnections have been built and refined over decades.
Throw in concerns about small form factor, low power consumption, quite fast a lot of unexpected hurles pop up.
My master's research goal was to produce a data mining/machine learning machine, or at the very least a data mining/machine learning co-processor. In retrospect, that was a very ambitious goal that would require many years of work, probably in collaboration with other graduate students.
What I ended up doing was just Support Vector Machines in digital hardware. Now granted, there is another aspect to my research that I'm not mentioning here, mainly that I didn't use normal floating point mathematical architectures, but a different innovative logarithmic based mathematical architecture. That in itself was a significant undertaking.
In any case, this sounds like a great project, I just wonder how much they can do in their (in an academic sense) very small time frame of 2-3 years. Even though a lot of preliminary work has probably already been done just to apply for the grant.
In any case, it is great to see something like this, something to keep in mind in case I ever go back for a Ph.D.
Once this technology has matured and some more headway can be made in Natural Language Processing, (uncertainty for teh win) we'll be on the cusp of some really excellent improvements in human-computer interfaces. It's becoming more common to see 'intelligent' systems being built to mirror the architecture of the human nervous system. This will be a necessary step to forming a generally proficient AI system. The day a computer can readily recognize you're being sarcastic, it's time to be paranoid.
"Don't waste your time or time will waste you" -MUSE
National Security Agency: "We did, and they are hooked to the national phone system."
making quantum leaps in speech recognition has tremendous potential for deaf and hard-of-hearing (I am the latter)
Imagine being in a meeting (almost always a problem for hearing impaired people) and having real-time subtitles.
$1 million is a TINY price considering upwards of 20% of the nation has some hearing loss and hearing aids cost on the order of $4000 a pair.
A year spent in artificial intelligence is enough to make one believe in God.