26 ms·

> 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 t

> 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 where I use it) would seem like a way to get some gain, but perhaps at the reduction of debuggability. > For example in voice search the actual web-scale search has to be done after the speech recognition. That's an area where literal exact transcription is usually required. But even then, Siri/Cortana/Alexa might be better off trying to figure out the meaning of what someone's asking rather than figuring out the exact words spoken in order to return the best results. Most people are quite bad at formulating good internet searches without a lot of trial and error. Let Google listen to a person talk about the problem they have or issue and then formulate the best results for that instead of forcing us to come up with the exact right phrasing to get appropriate results. It would help tremendously with the synonym and homophone issues that are so annoying now.

johnmcd3 9y ago
> 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...

johnmcd3 9y ago

> 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...