Speaker Verification
Hello everyone, how are you? I've been messing with Speaker_Recogniton_Verification.ipynb together with DiegoJota's issue: https://github.com/NVIDIA/NeMo/issues/2719. I got good results with the following script for extracting embs

```
verification_model = nemo_asr.models.ExtractSpeakerEmbeddingsModel.restore_from('Train_Verification.nemo')
emb = verification_model.get_embedding(Person1.wav).cpu()[0]
emb2 = verification_model.get_embedding(Person2.wav).cpu()[0]
score = np.dot(emb, emb2) / ((np.dot(emb, emb) * np.dot(emb2, emb2)) ** 0.5)
score = (score + 1) / 2
```


As I said I got good results managing to impose a limit for scores higher than 0.7 being considered good within the recognition between two different audios from the same speaker, examples:

AudioCaio1.wav x AudioCaio2.wav score: 0.74455544
AudioCaio1.wav x AudioCaio3.wav score: 0.73555545
AudioCaio1.wav x AudioCaio4.wav score: 0.78555545
......

But in cases of being different speakers the scores are very close to 0.69, 0.63, 0.67.... examples:

AudioCaio1.wav x AudioDiego1.wav score: 0.695478
AudioCaio1.wav x AudioDiego2.wav score: 0.635495
AudioCaio1.wav x AudioAntonio1.wav score: 0.627564
AudioCaio1.wav x AudioAntonio2.wav score: 0.687899
AudioCaio1.wav x AudioJose1.wav score: 0.656455
AudioCaio1.wav x AudioJose2.wav score: 0.679564
.....

We arrived at this result through the training present in Speaker_Recogniton_Verification which we generate the "Train_Verification.nemo" used in the verification_model = nemo_asr.models.ExtractSpeakerEmbeddingsModel.restore_from() script above.

As the results saw that the error margin is very close, we want to increase this error margin. Would it be possible to improve the margin of error through a more diversified training? Or with audios of a certain size? Would you have any recommended amount of training base?
Should the people I'm evaluating be present within the training set?

We didn't find many recommendations or documentation talking about how to be trained in recognition matters.

For information - We use the script only for voices in Portuguese, we do not intend to use it for voices in English or other languages.