[jugg1es]

So my problem wasn’t any other object interfering, I had to start/stop the savethatwave controller each time the recognizer was restarted

[jugg1es]

Yea, that worked great. I’m really surprised you don’t charge at all for OpenEars and the support you give on these forums. Do you have a ‘donate’ button anywhere?

[jugg1es]

Oh, I did not know about the vadThreshold. I will definitely play around with that.

I did play around with Rejecto’s weighting but I could never get it quite right. Since I’m doing a lot of processing on my end to determine whether a user is actually speaking to the software, I felt more comfortable having control than not receiving the event at all.

I’d love to use grammar rules, but, like I said, people rarely speak exactly what’s on the screen. So if the prompt is this:

Hello Molly, how are you doing today?

Users might actually say this (this happens way more often than you might think)

Molly Hello, how you doing today?

I were to use grammar rules, this wouldn’t be recognized.

Thanks for the tip on the vadThreshold, that might be just what I need.

[jugg1es]

My app involves using speech recognition to have a conversation with a simulated person to be used for training purposes. Like training a person how to interview for a job. The user has possibly dozens of prompts to choose from and I break each one into separate words and put them each into the language model. Then when the recognizer returns, I analyze the results and figure out which prompt they were actually trying to say. I did this way (as opposed to entering each prompt in it’s whole form into the model) for lots of reasons, the main one being that people often don’t read exactly what’s on the screen. It works great.

This means that there are a lot of possible things the recognizer can return as a hypothesis. Since it’s a training tool, there are often more than one person using it at once. It’s also common for it be used in a room with lots of other people talking. It would be nice if there was, for example, a input level threshold for when the recognizer thinks it’s being spoken to so it can tell if the user is speaking right at the device or whether it’s trying to listen to someone across the room.

Or if there are a lot of words spoken that aren’t in the language model, interspersed between words that ARE in the model, it will know that.

I tried Rejecto, but it was too strict and it wouldn’t return recognition events when it should have.

[jugg1es]

In case you were looking for some unsolicited advice, a paid plugin that is able to detect when the recognizer is listening but user isn’t trying to speak to the device would be very useful.

[jugg1es]

Yea I have all kinds of stuff going on, but not when the recognizer is active. I definitely narrowed the problem down to the recognizer itself but I also can’t get it to fail every time, even in my app. Overall it works great, but occasionally it will totally crash out on me. I’m going to add some timeouts and features to detect excessive noise and leave it at that until I can pinpoint the situation where it happens.

[jugg1es]

Meh, I can’t get it to fail using the sample app. If/when I do, I’ll send it. Thanks for responding.

[jugg1es]

Dang, I was hoping for an easy answer. I’ll do my best to see if I can get that to you.

[jugg1es]

And by long, I’m talking about over 20 seconds of just noise when it thinks someone is speaking

[jugg1es]

Please disregard my previous posts :) I got it working like I want it to.

The reason I was having trouble was due to a separate bug in my app. Thanks a lot for your help and supporting this great free tool!

[jugg1es]

Also, when I say there’s a delay when loading the model, I’m talking about the time it takes from when you load the model and start listening. It takes a second or two during the ‘calibration’ phase to actually start listening. So it’s not that the model takes a long time to load, but theres a significant time between loading the model and ‘Listening’ event.

[jugg1es]

Thanks for your quick reply.

I think that the Language Model will best suit my needs. The reason I’m concerned about it writing to disk is that I need to be able to load the models quickly (read: almost instantly) and since the app might need to generate a new model 40 times during one session, we’re talking about hundreds of files written to disk.

Is there a way to overwrite the current language model file each time? When I try to do this, I get errors that say either ‘failed to delete’ or ‘failed to access’ one or all of the model files.

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