4 ms·

In google chrome it reaches out to google to do the recognition. .NET seems to be built on the technology that has shipped with Windows for a while, and yet Co

by Eridrus 12y ago

In google chrome it reaches out to google to do the recognition. .NET seems to be built on the technology that has shipped with Windows for a while, and yet Cortana etc still reach out to the mothership for their speech recognition, so there are probably significant benefits to doing so, otherwise someone would have made theirs work offline and touted it. I'm guessing that matching a pre-specified pattern with some amount of error is easier than transcribing arbitrary voice, which you need for search. But I definitely agree, I regularly wish I could stop google maps' directions with a voice command when I get to a part of a route that I already know well enough to complete myself.

moron4hire 12y ago
With the cloud-based systems, it seems they made them from the perspective of their mobile devices, then ported the APIs to the desktop experience. The idea they are trying to put across is that it costs less energy on the phone to fire up the radio and send it to The Cloud--waiting a whole second just for network and processing queue latency--than it is to process locally. I don't think that is actually the case and it's just a sell to get all data to go through their systems. There aren't more popular client-side APIs because there aren't more popular client-only applications, and there aren't more popular client-only applications because developing the user experience is hard. I think, if you used one of these systems to figure out a good UX, then it would give you the impetus to chase down a good client-side solution for speech recognition.
- jhartmann 12y ago
  It is not just a sell. I build Neural Networks as part of some work I am doing, and the kinds of multi-layer deep networks that do the recognition just don't perform well on small devices. Also the best systems often have many different trained networks and specialized routing to find the best network for your accent, cadence, noise level, etc. Having all of these weights on your local device, running all the tests, running through any of the required preprocessing, and asking the network what the answer is, converting the results, etc. is going to be too much for your phone or low powered device. While there might be other reasons for them to send data to the cloud, to get the accuracy you really need beefy servers (potentially with GPU's or something more exotic like FPGA's), and potentially multiple servers working in concert.
  - moron4hire 12y ago
    Since I don't know the factors involved and you do, I must then ask how to reconcile this with the fact that the first version of Dragon NaturallySpeaking was released in 1997. My smartphone is definitely more powerful than even a top-of-the-line 1997 desktop PC. Now, I am sure Dragon V1 would be quite underwhelming to my modern notions of how computers should work. But I would even say my smartphone is better than a middle-of-the-road PC from 2010. It's better than my PS2, and even my PS2 had a few games that had speech interface (some Navy Seals game, it worked pretty well). What factors are at play? Is it the noise cancellation? Seems like a stationary device in a home has the ideal chance to create a reusable noise cancellation profile.

by Eridrus 12y ago

moron4hire 12y ago

With the cloud-based systems, it seems they made them from the perspective of their mobile devices, then ported the APIs to the desktop experience. The idea they are trying to put across is that it costs less energy on the phone to fire up the radio and send it to The Cloud--waiting a whole second just for network and processing queue latency--than it is to process locally. I don't think that is actually the case and it's just a sell to get all data to go through their systems. There aren't more popular client-side APIs because there aren't more popular client-only applications, and there aren't more popular client-only applications because developing the user experience is hard. I think, if you used one of these systems to figure out a good UX, then it would give you the impetus to chase down a good client-side solution for speech recognition.

jhartmann 12y ago
It is not just a sell. I build Neural Networks as part of some work I am doing, and the kinds of multi-layer deep networks that do the recognition just don't perform well on small devices. Also the best systems often have many different trained networks and specialized routing to find the best network for your accent, cadence, noise level, etc. Having all of these weights on your local device, running all the tests, running through any of the required preprocessing, and asking the network what the answer is, converting the results, etc. is going to be too much for your phone or low powered device. While there might be other reasons for them to send data to the cloud, to get the accuracy you really need beefy servers (potentially with GPU's or something more exotic like FPGA's), and potentially multiple servers working in concert.
- moron4hire 12y ago
  Since I don't know the factors involved and you do, I must then ask how to reconcile this with the fact that the first version of Dragon NaturallySpeaking was released in 1997. My smartphone is definitely more powerful than even a top-of-the-line 1997 desktop PC. Now, I am sure Dragon V1 would be quite underwhelming to my modern notions of how computers should work. But I would even say my smartphone is better than a middle-of-the-road PC from 2010. It's better than my PS2, and even my PS2 had a few games that had speech interface (some Navy Seals game, it worked pretty well). What factors are at play? Is it the noise cancellation? Seems like a stationary device in a home has the ideal chance to create a reusable noise cancellation profile.