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Speech Recognition Is Not Solved

awni.github.io

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Re: Speech Recognition Is Not Solved

#201

Post by Myrmornis »

pbhjpbhj wrote: post #162
joemag wrote: post #21

Mistakes in understanding speech are common even among humans. My wife and I have to repeat and clarify ourselves fairly frequently in our day to day conversations. She even jokes at times that I must check my hearing, because I often mishear what she said, while she thought she was being perfectly clear. I think where computers fall short is in two areas: 1) The rate of errors hasn’t hit the inflection point of bein…

One needs to know your wife to know that "hand me a tractor" is wrong. Perhaps she makes model farms as a hobby, perhaps you work in plant rental and that means "pass me the keys to one of the tractors", the possibilities are endless. You need vision, memory, profiling, etc., to even begin to properly contextualise day-to-day conversation.

Hm, I think I disagree with you and GP. The answer is Bayesian inference to both of you isn't it? The prior is going to give very low weight to your domestic partner asking you to "hand them a tractor", despite the fact that it's not impossible that it's the correct words.

Re: Speech Recognition Is Not Solved

#203

Post by computerex »

IshKebab wrote: post #98
gok wrote: post #73

Earlier quoted context omitted.

The TPU architecture isn't that weird...it's basically a hardware implementation of matrix multiplication. It also isn't a silver bullet for ASR, where neural networks are usually only used for a part of the recognition process.

Neural networks are used for nearly all of ASR now. Last I heard only the spectral components were still calculated not using a neural net and the text-to-speech is now entirely neural network (i.e. you feed text in and get audio samples out). I'd be surprised if they don't do that for ASR too soon if they haven't already.

AFAIK state of the art models are hybrid of HMM/GMM and CNN for phoneme classification. There are exotic CTC/RNN based architectures for end-to-end recognition but they aren't state of the art.

Re: Speech Recognition Is Not Solved

#204

Post by TheOtherHobbes »

baddox wrote: post #45
dmreedy wrote: post #37

Of course it's not solved. We don't even know how to define the problem. Speech is[1] a fundamental component of Language, which is a fundamental component of Intelligence[2]. This is addressed somewhat in the conversation around semantic error rate; that there is more to processing raw audio speech than the calculus of mapping signals to tokens; some understanding of semantics and context is required to differentiat…

Problem definition really is the issue. Even appealing to the Turing test is less than satisfying. I’m fairly sure I’m a human, but Siri and similar will likely outperform me in certain categories like place names and popular music.

Problem definition is not the issue. Contextual awareness is the issue.

Humans use different sub-languages to speak to friends, bosses, dates, lovers, spouses, teachers, students, and so on.

Each social context has its own vocabulary, its own set of expected conversation starter statements, its own set of likely responses, its own set of problems that may need to be solved - and so on.

A lot of what passes for intelligent interaction among humans is really just this social awareness.

Machines will fail the Turing Test without it. But you can't teach a machine to mimic social awareness by throwing 100,000 hours of speech samples at it.

Nor can you expect Echo/Siri/etc to know the context you're working in with no input from social cues - location, dress, time of day, social relationship, facial expression, etc.

So practical ASR turns into spoken-command-line-plus-guessing.

That turns out to be a pretty poor imitation of even the simplest social relationship.

It's not a useless imitation. Even today's limited voice assistants do a fair job at providing a useful service.

But the idea that you can drown neural networks in sample soup to train them, and build yourself a machine capable of intelligent-seeming conversation is just naive.

That's not how baby humans learn to hear and speak, and it's certainly not going to teach machines to converse at a human level.

Re: Speech Recognition Is Not Solved

#205

Post by abecedarius »

notahacker wrote: post #40
Radim wrote: post #19

Just the other day, I participated in a discussion about how "language identification" is a solved problem -- in fact, hasn't it been solved for a decade? As anyone who's had to use langid in practice will testify, it's solved only as long as: A) you want to identify 1 out of N languages (reality: a text can be in any language, outside your predefined set) B) you assume the text is in exactly one language (reality: c…

I'm reminded of how often I see Twitter offer to translate English-language tweets containing a proper noun or two from absurdly unconnected languages. And that's with text containing mostly common and distinctive English words.

I'm pretty sure Twitter's langid uses character n-grams. You'll see a tweet that's plain English that happens to match n-gram statistics unusually well with some other language, which pushes the likelihood score to just above English. (I checked this by running an example or two through my own langid code.)

It shouldn't be hard to improve on by treating the score on a tweet as bayesian evidence to combine with a prior from preceding tweets.

Re: Speech Recognition Is Not Solved

#206

Post by garfieldnate »

Don't forget speech of children and the elderly, which are also ifficult. I had to laugh a little at Indian accent recognition being so low in humans. I like Indian people but I've always hated the accent because it's so difficult to understand.

Re: Speech Recognition Is Not Solved

#207

Post by dunnodonna »

noir_lord wrote: post #147
spuz wrote: post #101

Earlier quoted context omitted.

I am sure you have thought about it but since you didn't mention it, are your symptoms those of Asperger's syndrome? Asperger's is often thought of as a sensory perception problem and Aspies tend not to be able to filter out sounds from background noise.

I took one of those online tests a while back that said if you get over 25 you need to see a specialist, I got 43. Not sure I have Asperger's though, I'm just a programmer who likes his own company.

Hmmm I can relate to what you are describing. I have some of that and was diagnosed with ADD in my 30s and I am a woman. Diagnose and medication helped immensely. It's as if you can't process too much signal at once. There is such a thing as asperger's spectrum. I cannot deal with high-pitched noises and noise in general really disturbs me.

Re: Speech Recognition Is Not Solved

#208

Post by brw12 »

I'm constantly surprised by the poor contextual quality of speech recognition. I think the basic audio recognition does well, but when there is ambiguity, it seems like systems that are popularly considered high-performing degrade drastically. For instance, I'm using Dragon NaturallySpeaking to dictate this right now, but if I say a certain punctuation mark at the end of a sentence, half the time it's going to say excavation mark!

Ditto with Google's Google Now assistant, or whatever the heck it's called these days. I have a Pixel 2 phone (Dragon heard "pixel to phone" -- it doesn't have up-to-date context on proper nouns in the news), but when I tried to create a calendar event using "Create calendar event... meet Bruno for pizza", it heard "MIT pronoun for pizza". It has hundreds of samples of my voice, and it already knew I was creating an event! "Meet" has to be one of the most common first words used in events.

It seems to me like there is pretty low hanging fruit, and that we need more focus on flexibility and resourcefulness rather than acting as though we're moving from 99.5% accuracy to 99.6%.

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