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I have one trouble with voice user interfaces: how difficult or feasible is it to localize them? I can easily translate the UI of a text-based application in a

I have one trouble with voice user interfaces: how difficult or feasible is it to localize them? I can easily translate the UI of a text-based application in an afternoon to my minority language. Especially if it uses gettext, it is a matter of editing a single text file. Will I be able to do that easily for a voice user interface?

vages 6y ago
No. Translation is not easy. I speak Norwegian. There are about five million language users. The Norwegian median wage is perhaps the highest in the world, possibly the top fifth, so there is a lot of pull to have Norwegian users. But only a few voice assistants have Norwegian interfaces (Siri was the first, Google may have joined the crowd). I work in IT, and making a conversational interface is rarely on the table; never if you exclude chat bots. To get an idea of the work involved, I know the guy who translated Siri through friends. Allegedly, that was at least six months of a single person working. I don't know if he only did the translation or if he also did a lot of AI work, but that's a lot in either case. The result isn't too impressive either. Siri has problems understanding my girlfriend because of her Bergen dialect, which differs from my Oslo dialect mainly in how the R sound is pronounced. My two cents.
- blueblisters 6y ago
  A good speech recognition model takes more than 10k hours of labeled speech data to train. The more diversity the better. Most production speech models (Siri, Google) need some domain-specific training as well - for offline or low-resource inference. Additionally, language models that help improve speech recognition accuracy need a fairly decent amount of unsupervised text as well. If you are building a conversational speech model, also consider the data needed to label intents for a large number of phrases. For a language like Norwegian, you are unlikely to find so much labeled and unlabeled data without forking out a large amount of money to literally ask some Norwegians to help. I suspect the work is being done, but it's extremely time consuming and researchers are likely spending time building models that can learn high-quality high-level representations of language that can be transferred to different scripts/languages with a small amount of data.
fdw 6y ago
That depends a bit on whether the language you want to support is already supported by your platform (Alexa, say). If it is, it can be as easy as gettext. It can also be a bit harder because you want to take cultural behavior for conversations into account. If your language isn't supported by the platform, you're just out of luck. The best you can do is request your platform to translate it, but that is - like vages described - a lot of work and will take time.

vages 6y ago

No. Translation is not easy. I speak Norwegian. There are about five million language users. The Norwegian median wage is perhaps the highest in the world, possibly the top fifth, so there is a lot of pull to have Norwegian users. But only a few voice assistants have Norwegian interfaces (Siri was the first, Google may have joined the crowd). I work in IT, and making a conversational interface is rarely on the table; never if you exclude chat bots. To get an idea of the work involved, I know the guy who translated Siri through friends. Allegedly, that was at least six months of a single person working. I don't know if he only did the translation or if he also did a lot of AI work, but that's a lot in either case. The result isn't too impressive either. Siri has problems understanding my girlfriend because of her Bergen dialect, which differs from my Oslo dialect mainly in how the R sound is pronounced. My two cents.

blueblisters 6y ago
A good speech recognition model takes more than 10k hours of labeled speech data to train. The more diversity the better. Most production speech models (Siri, Google) need some domain-specific training as well - for offline or low-resource inference. Additionally, language models that help improve speech recognition accuracy need a fairly decent amount of unsupervised text as well. If you are building a conversational speech model, also consider the data needed to label intents for a large number of phrases. For a language like Norwegian, you are unlikely to find so much labeled and unlabeled data without forking out a large amount of money to literally ask some Norwegians to help. I suspect the work is being done, but it's extremely time consuming and researchers are likely spending time building models that can learn high-quality high-level representations of language that can be transferred to different scripts/languages with a small amount of data.

fdw 6y ago

That depends a bit on whether the language you want to support is already supported by your platform (Alexa, say). If it is, it can be as easy as gettext. It can also be a bit harder because you want to take cultural behavior for conversations into account. If your language isn't supported by the platform, you're just out of luck. The best you can do is request your platform to translate it, but that is - like vages described - a lot of work and will take time.