As a deaf person I am using Ava [0] which uses IBM's speech to text service [1] as its backend AFAIK. I am always impressed by how it picks up on context clues to make corrections in realtime and capitalizing proper nouns (Incredible Pizza for example). However, it does not work with multiple speakers on a single microphone.
Speech Recognition Is Not Solved
61–70 of 208 posts
Re: Speech Recognition Is Not Solved
#62Too many people equate speech to text as 'speech recognition' and it clearly isn't. What is not a solved problem is Natural Language Processing. That NLP isn't solved, is a fertile source of papers. What is 'kinda' solved is the voice CLI, that is where speech is converted to text, that text is inserted in a class command interpreter, and if recognized the command is executed, otherwise it isn't. Nobody expects to be…
> Nobody expects to be able to type 'find me the files that changed yesterday' Google Photos is pretty close. "photos from december" works fine. "Find photos from decemeber" doesn't. I can type in "effects photos" and find photos Google applied effects to, which is pretty nifty. It is probably equivalent to the old game parsers in intelligence, but for a given domain it works pretty well. They could stand to filter o…
The query parser we had Blekko could do this as well. It could process 'photos' and 'december' as keywords and use them in a search. It would fail on 'photos last month' for the reasons I mentioned (it didn't really know december was a date it just looked for it to show up in the description or metadata)
That said, Google has done some great stuff in inferring things like units for conversion 10 feet in inches is easily parsed for example as a conversion request.
Re: Speech Recognition Is Not Solved
#63Too many people equate speech to text as 'speech recognition' and it clearly isn't. What is not a solved problem is Natural Language Processing. That NLP isn't solved, is a fertile source of papers. What is 'kinda' solved is the voice CLI, that is where speech is converted to text, that text is inserted in a class command interpreter, and if recognized the command is executed, otherwise it isn't. Nobody expects to be…
> I have no idea why they cancelled it.
My guess: because statistical methods are all the rage now, and have been for a while. This is famously illustrated by Fred Jelinek's quote "every time I fire a linguist, the performance of the speech recognizer goes up"[1], and cemented with the success of Google Translate.
Re: Speech Recognition Is Not Solved
#64> Latency: With latency, I mean the time from when the user is done speaking to when the transcription is complete. ... While this may sound extreme, remember that producing the transcript is usually the first step in a series of expensive computations . For many applications, making a transcription seems like an unnecessary step and source of errors. Skipping transcription when the user doesn't need it (most cases w…
> For many applications, making a transcription seems like an unnecessary step and source of errors. Skipping transcription when the user doesn't need it (most cases where I use it) would seem like a way to get some gain, but perhaps at the reduction of debuggability.
Agree. What's really needed is research into (and development supporting) how to combine the expertise from a speech recognition layer with the next layer in a machine learning process. That higher layer contains the domain specific knowledge needed for the problem at hand, and still leverage a speech layer focused on a broad speech data set and speech-specific learning (from Google, Microsoft, the community, etc.)
Today, how richly can information be shared? I see with Google's speech API you can only share a very finite list of domain-specific expected vocabulary.
Why not have speech tools at least output sets of possible translations with associated probabilities? Do any of the top tools allow this?
Then you could at least train your next level models with the knowledge of where ambiguity most exists, and what a couple of options might have been for certain words or phrases...
Re: Speech Recognition Is Not Solved
#65It is one thing to hear and correctly identify the words. It is another to understand the meaning. I've been thinking about this because my son has Auditory Processing Disorder, APD. He can hear great, even a whisper across the house. The trouble is the words don't always make sense. He can tell me the words he heard and they are correct, but assigning a meaning to them doesn't work like it does for most people. Afte…
Interesting. I wonder if your son will be smarter as a result, kinda like when neural networks generalize better if trained with a dropout.
Re: Speech Recognition Is Not Solved
#66Too many people equate speech to text as 'speech recognition' and it clearly isn't. What is not a solved problem is Natural Language Processing. That NLP isn't solved, is a fertile source of papers. What is 'kinda' solved is the voice CLI, that is where speech is converted to text, that text is inserted in a class command interpreter, and if recognized the command is executed, otherwise it isn't. Nobody expects to be…
I fully expect to be able to type (or say!) "find me the files that changed yesterday, sort by modification time, and display the first five images in that set".
The biggest failing of things like Siri and Cortana is they're not conversational, they're really not good at learning from example, they can only respond in hard-wired ways. "What's the weather?" will work. "Do I need a jacket tomorrow?" won't because they don't understand, instead giving the closest answer they can based on available information, not knowing how you prefer to dress or what you think is cold.
Until we can have full feedback neural networks we're not fully capitalizing on this AI stuff. Once we can cut the retraining time down to something marginal, maybe we can have computers "dream" and self-reprocess based on their accumulated learnings instead of waiting for a new kernel from the cloud.
Re: Speech Recognition Is Not Solved
#67Of 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.
Don't forget that being too good can also cause you to fail the Turing test. I would not expect a human to be able to answer some questions that may be trivial to a computer. Things like, "What's the square-root of 137?" Or, "Identify this obscure song within 5 seconds of listening from a random starting point."
Re: Speech Recognition Is Not Solved
#68It is one thing to hear and correctly identify the words. It is another to understand the meaning. I've been thinking about this because my son has Auditory Processing Disorder, APD. He can hear great, even a whisper across the house. The trouble is the words don't always make sense. He can tell me the words he heard and they are correct, but assigning a meaning to them doesn't work like it does for most people. Afte…
> Since then I've thought about voice recognition differently. The AI to understand the context or fill in the blanks is what will make or break it. Of course, and all humans rely on this as well. No one hears every word perfectly all the time --- it's impossible, because the source person doesn't pronounce every word perfectly all the time. Context clues are a huge part of speech recognition, as well as gestural typ…
This is why I think Google recently saying 95% word accuracy is just as good as a human is wrong. If I ask if you after dinner if you "Want to get a pizza cake", you'll probably quickly realize I mean "piece of cake". Mistakes like that in 1/20 words is a lot.
Google is the best of all the big players at figuring out this context (I once asked it what a Dead Left Shrimp was, apparently I was mishearing the name of basketball player Detlef Schrempf), but it will still search for "Pizza Cake" rather than correcting it.
Re: Speech Recognition Is Not Solved
#69Too many people equate speech to text as 'speech recognition' and it clearly isn't. What is not a solved problem is Natural Language Processing. That NLP isn't solved, is a fertile source of papers. What is 'kinda' solved is the voice CLI, that is where speech is converted to text, that text is inserted in a class command interpreter, and if recognized the command is executed, otherwise it isn't. Nobody expects to be…
"Speech Recognition" is an accepted term of art for "speech to text" or "transcription." "Speech Understanding" is a related but different problem.
I'd challenge you to transcribe a casual conversation without understanding. Too many words sound very similar, if not identical, and the context of the conversation dictates which word is in play.
Even something simple like "They're unhappy, Ness" could be interpreted as "Their unhappiness" unless you know Ness is a person in the converation.
Re: Speech Recognition Is Not Solved
#70Then there is Chinese, which the same "sound" can have many different words. And we constantly make up new short form or words, a lot more so then English means recognizing these are much harder. ( Then there is Cantonese... )
Sometimes I wonder, if Apps could add vocabulary to devices dictionary, so certain terms in Gaming, Fashion, or Tech or other domain, which are rarely used in normal conversation would be recognized in speech to text as well as Auto Correct.