Speech Recognition Is Not Solved
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Speech Recognition Is Not Solved
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Re: Speech Recognition Is Not Solved
#2As Portuguese native speaker forced to use foreign languages to talk to devices, it is not solved at all.
Re: Speech Recognition Is Not Solved
#3Just the other day I was arguing (pleasantly) with someone here on HN saying that AI has solved speech rec (among other tasks).
https://news.ycombinator.com/item?id=15429287
Wish this article was written a few days earlier.
An analogue of this article exists for most other domains claimed to have been solved.
Re: Speech Recognition Is Not Solved
#4A big thing for me is the difference between offline and online speech recognition. This really ties in with the last point, advancements need to be efficient in order to be used.
I'm not sure how much work it would take to scale down Apple's voice recognition to run on the device or if it is feasible with their model, but currently it can take 5-10 seconds longer to get an answer from Siri during peak times.
Re: Speech Recognition Is Not Solved
#5[flagged]
Re: Speech Recognition Is Not Solved
#6What I would like to see you would be an open source speech recognition system that is easy to use, and works. There is really no match for the proprietary solutions in the open source world right now, and that is disappointing.
Not to say that there aren't open source speech recognition systems, they just aren't completely usable in the way proprietary solutions are. A lot of research goes into open source speech recognition, where they are lacking is in the datasets and user experience.
Hopefully, Mozilla's Common Voice project https://voice.mozilla.org/ will be successful in producing an open dataset that everyone has access to and will spur on innovation.
Re: Speech Recognition Is Not Solved
#7I remember using the Merlin Microsoft Agent to create speech-based "apps" in 2001, including text-to-speech as well as speech recognition. I made a SAT preparation that would test me by presenting a word and I had to say the meaning. I also made a Spanish learning app using Merlin, where it would read out the spanish word (in spanish!) and I would have to say the english one. It worked really well. Its been 16 years since then, and I have to say that I had expected this area to have been completely "solved" by now.
Re: Speech Recognition Is Not Solved
#8What I would like to see you would be an open source speech recognition system that is easy to use, and works. There is really no match for the proprietary solutions in the open source world right now, and that is disappointing. Not to say that there aren't open source speech recognition systems, they just aren't completely usable in the way proprietary solutions are. A lot of research goes into open source speech re…
I agree - I don't care how good Google gets it, this is an unsolved problem until I can do it with open source tools operating without an internet connection.
Re: Speech Recognition Is Not Solved
#9As Portuguese native speaker forced to use foreign languages to talk to devices, it is not solved at all.
Even English doesn't work the way it should. Remember Apple had to rush out a patch shortly after Siri was released because it couldn't understand Australians.
Siri still doesn't understand the majority of my Minnesota relatives.
Re: Speech Recognition Is Not Solved
#10It's always been a puzzle to me that published WER is so low, and yet when I use dictation (which is a lot--I use it for almost all text messages) I always have to make numerous corrections.
This sentence is crucial and suggests a way to understand how WER can be apparently lower than human levels, yet ASR is still obviously imperfect:
> When comparing models to humans, it’s important to check the nature of the mistakes and not just look at the WER as a conclusive number. In my own experience, human transcribers tend to make fewer and less drastic semantic errors than speech recognizers.
This suggests that humans make mistakes specifically on semantically unimportant words, while computers make mistakes more uniformly. That is, humans are able to allocate resources to correct word identification for the important words, with less resources going to the less important ones. So maybe the way to improve speech recognition is not to focus on WER, but on WER weighted by word importance, or to train speech recognition systems end-to-end with some end goal language task so that the DNN or whatever learns to recognize the important words for the task.