This is a problem that’s plagued me for quite a while. I’m not a computational linguist myself, but one of the reasons that theoretical linguistics is important is that it allows us to create robust concpetional models of language… which is basically what voice recognition (or synthesis) programs are. But, you may say to yourself, if it’s your job to create and test robust models, you’re clearly not doing very well. I mean, just listen to this guy. Or this guy. Or this person, whose patience in detailing errors borders on obsession. Or, heck, this person, who isn’t so sure that voice recognition is even a thing we need.Now, to be fair to linguists, we’ve kinda been out of the loop for a while. Fred Jelinek, a very famous researcher in speech recognition, once said “Every time we fire a phonetician/linguist, the performance of our system goes up”. Oof, right in the career prospects. There was, however, a very good reason for that, and it had to do with the pressures on computer scientists and linguists respectively. (Also a bunch of historical stuff that we’re not going to get into.)
Basically, in the past (and currently to a certain extent) there was this divide in linguistics. Linguists wanted to model speaker’s competence, not their performance. Basically, there’s this idea that there is some sort of place in your brain where you knew all the rules of language and have them all perfectly mapped out and described. Not in a consious way, but there nonetheless. But somewhere between the magical garden of language and your mouth and/or ears you trip up and mistakes happen. You say a word wrong or mishear it or switch bits around… all sorts of things can go wrong. Plus, of course, even if we don’t make a recognizable mistake, there’s a incredible amount of variation that we can decipher without a problem. That got pushed over to the performance side, though, and wasn’t looked at as much. Linguistics was all about what was happening in the language mind-garden (the competence) and not the messy sorts of things you say in everyday life (the performance). You can also think of it like what celebrities actually say in an interview vs. what gets into the newspaper; all the “um”s and “uh”s are taken out, little stutters or repetitions are erased and if the sentence structure came out a little wonky the reporter pats it back into shape. It was pretty clear what they meant to say, after all.
So you’ve got linguists with their competence models explaining them to the computer folks and computer folks being all clever and mathy and coming up with algorithms that seem to accurately model our knowledge of human linguistic competency… and getting terrible results. Everyone’s working hard and doing their best and it’s just not working.
I think you can probably figure out why: if you’re a computer and just sitting there with very little knowledge of language (consider that this was before any of the big corpora were published, so there wasn’t a whole lot of raw data) and someone hands you a model that’s supposed to handle only perfect data and also actual speech data, which even under ideal conditions is far from perfect, you’re going to spit out spaghetti and call it a day. It’s a bit like telling someone to make you a peanut butter and jelly sandwich and just expecting them to do it. Which is fine if they already know what peanut butter and jelly are, and where you keep the bread, and how to open jars, and that food is something humans eat, so you shouldn’t rub it on anything too covered with bacteria or they’ll get sick and die. Probably not the best way to go about it.
So the linguists got the boot and they and the computational people pretty much did their own things for a bit. The model that most speech recognition programs use today is mostly statistical, based on things like how often a word shows up in whichever corpus they’re using currently. Which works pretty well. In a quiet room. When you speak clearly. And slowly. And don’t use any super-exotic words. And aren’t having a conversation. And have trained the system on your voice. And have enough processing power in whatever device you’re using. And don’t get all wild and crazy with your intonation. See the problem?
Language is incredibly complex and speech recognition technology, particularly when it’s based on a purely statistical model, is not terrific at dealing with all that complexity. Which is not to say that I’m knocking statistical models! Statistical phonology is mind-blowing and I think we in linguistics will get a lot of mileage from it. But there’s a difference. We’re not looking to conserve processing power: we’re looking to model what humans are actually doing. There’s been a shift away from the competency/performance divide (though it does still exist) and more interest in modelling the messy stuff that we actually see: conversational speech, connected speech, variation within speakers. And the models that we come up with are complex. Really complex. People working in Exemplar Theory, for example, have found quite a bit of evidence that you remember everything you’ve ever heard and use all of it to help parse incoming signals. Yeah, it’s crazy. And it’s not something that our current computers can do. Which is fine; it give linguists time to further refine our models. When computers are ready, we will be too, and in the meantime computer people and linguistic people are showing more and more overlap again, and using each other’s work more and more. And, you know, singing Kumbayah and roasting marshmallows together. It’s pretty friendly.
So what’s the take-away? Well, at least for the moment, in order to get speech recognition to a better place than it is now, we need to build models that work for a system that is less complex than the human brain. Linguistics research, particularly into statistical models, is helping with this. For the future? We need to build systems that are as complex at the human brain. (Bonus: we’ll finally be able to test models of child language acquisition without doing deeply unethical things! Not that we would do deeply unethical things.) Overall, I’m very optimistic that computers will eventually be able to recognize speech as well as humans can.
- Speech recognition has been light on linguists because they weren’t modeling what was useful for computational tasks.
- Now linguists are building and testing useful models. Yay!
- Language is super complex and treating it like it’s not will get you hit in the face with an error-ridden fish.
- Linguists know language is complex and are working diligently at accurately describing how and why. Yay!
- In order to get perfect speech recognition down, we’re going to need to have computers that are similar to our brains.
- I’m pretty optimistic that this will happen.