Understanding diarization labels about uis-rnn HOT 6 CLOSED

I really don't understand your questions. Please clarify.

We can't understand the relationship between real tags and predictive tags.

Which part you don't understand?

it makes it impossible to find out who the speaker is.

What do you mean?

from uis-rnn.

For example, I used 46 people to train the model, where train_cluster_id is [0,0,0............... 45,45,45], and then I used Forty-sixth people to predict, where test_cluster_id is [0,0,0,0,0...]. The predicted result is [0, 0, 0, 0, 0...]. My question is, shouldn't the predicted label be [45, 45, 45...]? I hope you can understand what I said.

from uis-rnn.

In diarization, the labels are not absolute labels, but relative labels. It is identity-agnostic.

Labels are meaningless across utterances.

For example, in an utterance, the labels are [0, 0, 1], it means first two segments are from one speaker, while the last segment is from a different speaker. It does NOT refer to any specific speaker.

if another utterance has labels [0, 1, 1], the two speakers in this utterance has no connection with the speakers in the previous utterance.

from uis-rnn.

I understand exactly what you said. Can I get the absolute label? Because I want to know who the speaker is.Thank you。

from uis-rnn.

from uis-rnn.

If you want the absolute labels, you are looking at the wrong technique and the wrong repo. It's not the problem diarization is trying to solve. It's speaker recognition, which is much easier than diarization. You can simply compute cosine similarity with different embeddings.

from uis-rnn.