And so FB for instance can send some voice data to their servers and get a text output. And then FB can use text sentiment analysis to get further context about the message.

Sadly, most people don't have the speech data to train their own recognizers on large vocabulary systems, and that's even harder for languages that are not English. With exception of Google/Amazon/FB/Microsoft/Baidu/etc other people have to use the API's offered by the above companies to do high fidelity recognition. Which sucks because there is a cost to each recognition. You have to pay someone else to do it.

Whereas FB/Amazon/MS/Baidu/etc can do high fidelity recognition offline on large vocabulary and offer it as a service. THIS is why FB wants to make speech recognition systems.

I wonder if you could bootstrap a sizable speech dataset by trawling audio off YouTube and then using one of the really good cloud speech recognition services to label it. :)

There have been much better, larger datasets available for a long time, for example the Fisher English conversational telephone speech corpus was released in 2004 and contains ~1950h of transcribed speech. There are tons of other datasets in various languages and for various applications (conversational speech, broadcast transcription, etc.).

Edit: It's even less [1]:

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Is the implication that offline Android recognition does not train on the owner's voice at all? I imagine a lot of phones these days are at least as powerful as the Pentium 200s used to train (successfully!) Dragon Dictate et al 20+ years ago.

Secondly, when I say "train", it is in a totally different context than how you seem to be using the term. You are using it in the context of adapting an acoustic model to a individual speaker to improve the performance. I am talking about building the initial model. Typical RNN or even convolution based algorithms require a lot of time and processing power to train. What's even harder to get than the processing power though is of course, data to train off of.

Secondly, the trained model itself is very big just for storage, and inference against the model is also resource intensive. This is why Android/Google maps/search/etc go out to the google's backend recognition servers for speech to text before falling back on the shitty (but relatively good) offline inline model (that may not even be using state of the art speech recognition techniques and may be using old school GMM based recognizers).

Finally, the large models trained on the backend servers using their distributed computing infrastructure are extremely more accurate than the shitty fallback model, so speaker dependent adaptations aren't necessary. If you can get very very good performance from a speaker independent model, why would you put the extra effort to make speaker dependent adaptations if the gain is very marginal? Not to mention the fact that speaker independent models are more useful in more situations and are extremely powerful. Google for instance can caption videos automatically using speech recognition, which is amazing. If the models were speaker dependent they wouldn't be able to do that. That's why the focus has been so much towards speaker independent models.

I totally disagree. Compared to Sphinx it is still lightyears better.

To wit I use it for my android based home automation voice recognition and even from a distance with background noise it still works about >90% accuracy. My original tests with Sphinx in a similar environment garnered about 30%.

There’s also the new Portal hardware.