how to use it for speaker verification about rawnet HOT 6 CLOSED

Hi Willy,

I think the task you are mentioning would be open-set speaker identification.

Assuming you're using a neural network to do it, there are two approaches.

1. Train a neural net with N speakers. In the test phase, you feed the input to the same network and select the highest post-probability. This would be a closed-set speaker identification because you cannot classify newly emerging speakers.
2. Use a pre-trained speaker embedding extractor to enroll N speakers. Then for each input, compare the input utterance's speaker embedding with N speakers' embeddings to select the speaker with the highest similarity. This would be the open-set speaker identification. I believe this coincides with your description.

On the other hand, in speaker verification, you normally have a claimed (or target) speaker and an input utterance. You compare the two and make a binary decision, about whether they are the same or not.

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Thanks JungJee!

You are right, my task is for open-set, speaker verification, which is the case in your description in item-2.

More question:
When I train the speaker embedding, I used X speakers.
In verification case, we have N speaker (including target speaker T), each has M utterances. Then I should have NM embeddings? The target speaker T's new input utterance (not included in speaker T's enroll utterance-set), and calculate this input utterance's embedding also. Then compare with the NM embeddings, and make binary decision.

1. I think X >>> N. For a given N, say N=10, what value of X should be suitable for acceptable performance? Will size of X influence model size much?
2. None of the N enroll speaker is included in X. If want to get better performance, should the N speaker's utterances be used in training the model, like fine-tuning?
3. Can M=1? I think bigger will give better accuracy.

Could you please share your opinion on these question?
Thanks,
Willy

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Maybe I'm the one who's confused. In my understanding, if what you want is not an "open-set speaker identification" but "speaker verification" you can simply compare the speaker embedding of the enrollment and the test utterance. If there exist multiple enrollment utterances (from your explanation, I think it is "M"), you can average their speaker embeddings to derive one speaker embedding representing one speaker.

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Hi Jungjee,

Thanks for your reply. In my understanding, "open-set" means the models can be used to recognize any speaker, and the speaker will sure not be included in the training set of the models.
I am doing speaker verification. In the system, there will be N speakers in total. For speaker n, there are M utterances used to enroll, and I got M embeddings for speaker n. Can i average the M embeddings into one? For example, the embeddings are K dimensions, so just get an average the M vectors and save one vector?
Can I save the M embeddings and use them all? when test a utterance, for each embeddings, I got a score with an embedding, and then i average the score, and use this score as the final one to judge if accept or reject the test utterance. The problem is, calculating M embeddings will be much slower compared with calculating only one embedding.
Please let me know your opinion.

Thanks,
Willy

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