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I was making a simple phoneme classification model for a 10 week-long class project and I ran into a small question. Is it possible, to create a model that takes a 1-second (the longest phoneme is 0.2 second but the large image is kept for context) spectrogram as input? Some people suggest creating an RNN for phoneme classification but can you build a pure CNN phoneme classification model? Any responses would be great.

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Yes you can, a few years ago I made a simple CNN for a single Arabic phoneme classification. You can use spectogram or using MFCC / MFSC as features, as long all data has the same size (use padding or cropping if needed).

You may need RNN if you want to combine some phonemes to recognize a single word or longer.

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  • $\begingroup$ Wow, thanks a lot! Any suggestions for the model shape (how many layers and how many filters)? $\endgroup$ – Hozaifa Bhutta Nov 17 '19 at 3:09
  • $\begingroup$ Actually, it all depends on your case, for a simple case I think you only need one or two convolutional layers and a single fully-connected layer. I usually use around 30 filters. But, just try it to find your best values :) $\endgroup$ – malioboro Nov 17 '19 at 3:16
  • $\begingroup$ Okay, well I actually I have a full model written down but it isn't running well (not increasing in accuracy). Where should I post my code and give a better description of my model? $\endgroup$ – Hozaifa Bhutta Nov 17 '19 at 3:52
  • $\begingroup$ You can ask another question if you think there are some errors in your models. But, you may ask an implementation question that out of topic in AI.SE if your question is about errors in your code/implementation, you should ask that in Data Science SE $\endgroup$ – malioboro Nov 17 '19 at 7:39
  • $\begingroup$ Okay thanks, I put the question on this link, so you can look over it if you want to, 'datascience.stackexchange.com/questions/63376/…' $\endgroup$ – Hozaifa Bhutta Nov 19 '19 at 3:32

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