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In this tutorial, they build a speech recognition model to classify a one-second audio clip as one of ten predefined words. Suppose that we modified this problem as the following: Given an Arabic dataset, we aim to build a dialects recognition model to classify a two-second audio clip as one of $n$ local dialects using ten predefined sentences. I.e. for each of these ten sentences, there are $x$ different phrases and idioms which refer to the same meaning$^*$. Now how can I take advantage of the mentioned tutorial to solve the modified problem?

$*$ The $x$ different phrases and idioms for each sentence are not predefined.

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  • $\begingroup$ Is my modified problem similar to this one? $\endgroup$ – Abdulkader Mar 9 at 9:46
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The tutorials you link are not much relevant, there are already existing implementations of your exact problem.

You can use https://github.com/swshon/dialectID_e2e, there are many other similar implementations on github.

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  • $\begingroup$ Can you explain more how they are not relevant? Thank you. $\endgroup$ – Abdulkader Apr 13 at 21:17
  • $\begingroup$ You can use those things too, they are just not as related as the project I linked you. $\endgroup$ – Nikolay Shmyrev Apr 13 at 21:20

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