Speaker verification with low confidence
Hello

I am using NeMo for my Portuguese language transcriber and I have great results, far superior to other engines, congratulations on the job.
Now I'm working with NeMo to use voice authentication, in my scenario I'm using phone call audio as a base, in 8K and with background noise. I tested all the pre-trained models that you suggest, the "best" being titanet_large and the result from the same people is giving between 50 to 65% confidence. To test I used the code:

`
speaker_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name='titanet_large')
speaker_model.verify_speakers('/content/PERSON1-AUDIO1.wav', '/content/PERSON1-AUDIO2.wav')
`

And the sample audios (I can't provide more due to privacy concerns):
[NVIDIA.zip](https://github.com/NVIDIA/NeMo/files/10663690/NVIDIA.zip)

As you suggest, I did a finetunning using the same parameters suggested in the training https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb.
I only changed the number of Epochs, placing 250, this training ended after 1 day, the loss started at 13.7 and after 250 Epochs it was at 0.0000508

With this model in hand, I did the same tests, but the result got worse, it only gave high confidence when the person said the same thing said in the other audio. It gave about 90% if both audios had "Hello", for example, otherwise it gave 50%.
For this finetunning, I used approximately 60 hours of phone call audio as a basis, with approximately 1100 different speakers.

I opened an old issue with you about this same problem, but I still didn't see any improvements. I tested what was suggested from 6 seconds upwards and there were no improvements. I'm following titanet's own paper that comments that we should use at least 3 seconds
https://github.com/NVIDIA/NeMo/issues/2719

Now comes my questions:
Is NeMo suitable for my use case? Phone call audios that are in 8K (For training and testing I convert to 16K).
Do I need to test new parameters? What do you suggest for values ​​and what can I adjust.
Does spoken language matter for training? My test/training audios are in Portuguese.