Given the scenario:
- We have a speech recording from an unknown person.
- We have a speech recording from a known person.
- We have a large database of speech recordings from different persons.
We would like to decide if the unknown speaker's identity is the same as the known speaker's identity, are they the same person?
It looks like for me as a supervised learning task: we show to the system a suspect (known person) and a general type from the database. The the system decides if the unknown recording is closer to the suspect or the general model.
But as far as I know, this kind od speaker recognition system uses GMM-UBM clusterization or DNNs which are unsupervised methods, right? What am I missing?