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?"

Comments

Carnivore on telephones

[CrazyJim1]

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

accuracy

[tubbtubb]

100 to 1000 times more efficient worth $1M? meh. maybe.
100 to 1000 times more accurate worth $1M? definitely.

Good use of $1 million?

[Anonymous Coward]

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!

Re:Good use of $1 million?

[Armchair Dissident]

Every time a dollar value is placed on a piece of research, some idiot comes along and say "Hey! This could be spent providing clean drinking water, and food and shelter", as if only research that directly provides clean drinking water or food or shelter is worth funding. Quite frequently the idiot making this statement is in a perfect position to provide money to ensure that more people have access to these facilities, and just as frequently that idiot isn't doing so.

I'm sure that when America and Russia were engaged in the space race there were people saying "Hey! This money could be better spent on disaster relief!". And where are we now? Only a few short decades later we have sattelites that tell us where hurricanes are going so that we can evacuate areas and people who would otherwise die surviveWe have a global reliable telecommunications satellites so that disaster relief agencies in third world countries can inform people of what supplies are required, and people who would otherwse die survive.

Without the massive investment in jet airline technology that could otherwise have been spent "saving the starving", we would not be able to travel to disaster areas within hours of an incident. And so the list goes on.

If you personally want to see more money invested in agencies that provide disaster relief, or reliable shelter or clean water then you only have to donate to the right charities, and encourage others to do the same. It doesn't take many people to donate out of their pockets to provide $1 million. You can start here.

Natural Language Interpreter

[MankyD]

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.

Re:Natural Language Interpreter

[Masker]

Natural language processing and speech recognition are two entirely separate problem spaces.

Natural language processing tasks involve parsing strings of tokens and mapping them to commands to be executed. So, from your example, "Pull up the name of employee number 12345", the natural language system must map "Pull up" to "SELECT", "the name" to "name", "of employee number 12345" to "FROM users where id = 12345". Really, it's largely a problem of context, and your example shows an excellent problem: the "of employee number 12345" to "FROM..." map requires the contextual information of where to pull this information from. Surely multiple tables of a database could have an "employee number" field in them. Do you want all of the tuples which matches, or just from a certain table? Now, in the context of looking up a bunch of other employees, maybe I know what table you've been hitting a lot, and can determine what you're asking, but without that context, I have no idea.

In fact, everyday speech has a lot more ambiguity in it than could be handled without keeping large amounts of state, be it contextual or experiencial/situational. For example, if I overhear two people in a conversation, and the first thing I hear is: "Yeah, but he's been lying all though his campaign, and I for one don't support him," I have no idea which politcal candidate might be speaking of. However, if I saw that person wearing a shirt for a political campaign last week, then I have enough context to make a reasonable guess that he's talking about that person's opponent.

Speech recognition is a "lower level" than that: it's about matching acoustic information into speech sounds and then using the speech sounds to determine the word that was said. This is a hugely complex task that has a number of unsolved problems (of which these are the 3 that I can think of off the top of my head):

1) "speech sounds" are fuzzy categories, and are not canonical targets.
2) salient "features" of phonemes are disputed, contradictory and large amounts of redundancy/conflicting info are built into the speech signal
3) idiosyncratic speaker-to-speaker differences make the phoneme categories even fuzzier and can complicate the task even for the one speech recognition system that we know works: the human brain.

At any rate, the problems that need to be solved for speech recognition are not the same problems in natural language processing. While there may be some cross over in pattern-matching, the specifics of the problem spaces make it unlikely that you will get much benefit for NLS (natural language systems) from just making the algorithms faster.

Which, in fact, is my main criticism of this article: the algorithms that we have now are piss-poor, and making them faster doesn't intrinsically make them better. Unless there's been some huge advance in the field that I'm unaware of, you'd still have to train a SRS (speech recognition system) on your idiolect, by reading some pre-selected passages to it. This model has lots of problems, most specially that it's tailored to an individual. Imagine if you had to have each person that you spoke with read some canned paragraphs to you the first time you met so that you could interact....

[sorry I don't have sources for all of this; I'm AFB, and I don't have time to dredge up info right now. But, apparently, I have time to write one long-ass entry...]

The difficulties of dialect...

[L0neW0lf]

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.

hardware accelerated

[GMail Troll]

"People who are serious about software should make their own hardware" - Alan Kay

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

Re:1... million... DOLLARS!!!

[randombit]

- Voice controlled robots ("You missed a corner, vacuum cleaner")
- Data search by voice ("Find me a channel that plays Star Trek")


Kinda jumping ahead of yourself, aren't you? There are two steps to an operation like these, speech to text, and understanding the text you get out. Speech recognition gives you the first part, but you still have to be able to pull apart the sentence and figure out what it means.

Also, the article didn't say more accurate than software, it said more efficient. You know, uses less power and stuff like that? If the applications you mention (like search via voice) were possible/usable, you could run them today on an upper-end PC no problem.

Re:Funny...

[loginx]

I want to sing the general tone of a song I heard on the radio in a microphone and have google direct me to that album on froogle.

THAT would be awesome!

Re:Funny...

[Christopher Thomas]

I work on product X and think of all the possibilities (list slightly feasible but most likely never going to happen features).

If this is really true what they're saying then people should put tons more money into product X!

Actually, use of speech recognition technology to index video clips for search engines _is_ both a very desirable technology, and something that can be done fairly easily (most professionally produced video, at least, takes great pains to have one speaker at a time and keep noise to a minimum). There's a fair bit of video content accessible via the web right now, and this will only increase (most new digital cameras can take video clips now - remember how quickly still pictures flooded the web when digicams first became available?).

Speech recognition technology has trouble when it's trying to sort out a noisy environment or a degraded communications channel, and has trouble holding useful open-ended conversations (as opposed to task-driven), but it's very capable in most other contexts. After all, the field has been under study for decades.

In summary, your mocking of the parent post is premature.

speech recognition and deaf/hard-of-hearing

[CrudPuppy]

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.