<p>You should check out the end-to-end speaker verification systems, which are essentially siamese networks for speaker verification.</p>

<p><a href="https://arxiv.org/abs/1710.10467" rel="nofollow noreferrer">L. Wan, et al., "Generalized end-to-end loss for speaker verification," in Proc. ICASSP, 2018.</a></p>

<p>I think the literature above may give you intuition on your problem.</p>
Creating Pairs for Training a Siamese Network for Speaker Verification Text Dependent
<p>I want to build a siamese network for speaker verification using <code>python</code>. This network consists of 2 identical Convolutional Neural Network (CNN) to learn a similarity function which can distinguish whether 2 input voice belong to the same person or not. <br><br></p>

<h3>Data</h3>

<p>I have 10 person recording their voice in <code>.wav</code> saying digits of 9 number in Bahasa <code>(satu, dua, tiga, empat, lima, enam, tujuh, delapan, sembilan)</code> each person records 5 times for each number, so each person have 45 recordings <code>(9 x 5)</code>. I used MFCC to get features vector and got vector shape <code>(450, 250, 13)</code> -- (rows, number_frames, number_cepstral) and now I want to make a pair of my data</p>

<p>I have seen these link <br>
- <a href="https://www.kaggle.com/arpandhatt/siamese-neural-networks" rel="nofollow noreferrer">https://www.kaggle.com/arpandhatt/siamese-neural-networks</a> <br>
- <a href="https://keras.io/examples/mnist_siamese/" rel="nofollow noreferrer">https://keras.io/examples/mnist_siamese/</a></p>

<p>But I cannot understand what kind of methods which being used to create the pairs. Given my data, how can I create good pairs to train the siamese network?</p>

<p><b>Notes</b>: I want to build speaker verification text dependent which means one recording saying <code>'satu'</code> will be compared to another recording saying <code>'satu'</code> too.</p>