Reply To: Background noise causing OpenEars missing words

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Halle Winkler

Hi Billy,

Apologies again for the very long delay and thank you again for your patience. This has been an unusual situation with a large series of prerequisites to being able to make a release and a rare case in which smaller releases were not possible. So, I have been able to run your case. When I use this audio file as the test file with RapidEars, I receive about the results I would expect: mostly correct recognitions, but sometimes there are dropped recognitions when the same word is repeated and sometimes there are wrong recognitions due to the noise level (and also probably due to the fact that I suspect a high female voice is underrepresented in the data used to generate the acoustic model).

I have the impression that my results differ from yours because you said above that you had absolutely no success, so presumably no correct recognitions. I noticed a couple of things about your case that may have affected this. The first is that your example alternates between using the stock OEPocketsphinxController listening method and RapidEars, so it is possible that you were expecting a callback in a RapidEars callback but it was being returned in a normal and unlogged OEPocketsphinxController callback. The other thing is that there is a missing comma in the language model array in your case, which could affect your vocabulary since I think it causes the two strings with no comma to be appended to each other as a single word.

I would imagine due to the long delay that you are no longer working on this issue and have other workarounds, however, you can let me know if that is not the case. If your results were similar to mine and you saw a mix of working and non-working recognitions, I would probably have to say that this is the situation I mentioned above: noisy recognition is quite difficult and it is probably expected to have compromised recognition, particularly for cases where there is a voice that is particularly high, or may have an accent, or other outlying characteristics when compared with the majority of the acoustic model data. One way of addressing this could be by doing acoustic model adaptation; search the forums and site for more info on this.