<p>The tutorials you link are not much relevant, there are already existing implementations of your exact problem.</p>

<p>You can use <a href="https://github.com/swshon/dialectID_e2e" rel="nofollow noreferrer">https://github.com/swshon/dialectID_e2e</a>, there are many other similar implementations on github.</p>

CC BY-SA 4.0
2019-04-13T21:17:52.507Z
Can you explain more how they are not relevant? Thank you.
Abdulkader
CC BY-SA 4.0
2019-04-13T21:20:03.273Z
You can use those things too, they are just not as related as the project I linked you.
Nikolay Shmyrev
CC BY-SA 4.0
2019-04-13T20:58:33.217Z
2019-04-13T20:58:33.217Z
Nikolay Shmyrev
<p>In <a href="https://www.tensorflow.org/tutorials/sequences/audio_recognition" rel="nofollow noreferrer">this</a> 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 <em>dialects</em> recognition model to classify a <em>two</em>-second audio clip as one of <span class="math-container">$n$</span> local dialects using ten predefined <em>sentences</em>. I.e. for each of these ten sentences, there are <span class="math-container">$x$</span> different phrases and idioms which refer to the same meaning<span class="math-container">$^*$</span>. Now how can I take advantage of the mentioned tutorial to solve the modified problem?</p>

<p><span class="math-container">$*$</span> The <span class="math-container">$x$</span> different phrases and idioms for each sentence are not predefined.</p>

CC BY-SA 4.0
2019-03-09T09:46:29.523Z
Is my modified problem similar to this [one](https://arxiv.org/ftp/arxiv/papers/1707/1707.09917.pdf)?
Abdulkader
<deep-learning><natural-language-processing><classification><voice-recognition>
How much the dialects recognition and speech recognition are relevant?
Abdulkader
# How much the dialects recognition and speech recognition are relevant?

(Asked by: username_0 on 2019-03-09)

In [this](https://www.tensorflow.org/tutorials/sequences/audio_recognition) 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.

**Tags:** deep-learning, natural-language-processing, classification, voice-recognition

> **Comments:**
> **username_0**: Is my modified problem similar to this [one](https://arxiv.org/ftp/arxiv/papers/1707/1707.09917.pdf)?  

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## 1 answers

### Answer by username_1 on 2019-04-13. Score: 1 | Accepted answer

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.

> **Comments:**
> **username_0**: Can you explain more how they are not relevant? Thank you.  
> **username_1**: You can use those things too, they are just not as related as the project I linked you.  

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