Closed hadware closed 4 years ago
Can you please define "speaker unrolling"? I am not familiar with this wording.
Did you mean "speaker enrollment"? If so, what do you have in mind exactly? Speaker identification?
Hello,
Yes, it would be speaker enrollment. Based on variable amount of target speakers, find all the segments in the audio from these speakers. We were thinking to a pipeline that look like this https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/41939.pdf
I didn't go through the paper but I think the SpeechTurnClosestAssignment pipeline might get you started.
Enrollment
Basic idea: gather all speaker embedding for each target and take the average.
Recognition
Basic idea: for each test speech turn (or, here, speaker cluster), find closest target speaker (by comparing their average embedding. You might also want to consider the reject option if even the closest target speaker is too far.
Amazing! Thank you! We will let you know how this goes and if it works on our 'special' data.
Closing this issue as I believe the original question has been answered. I'd still be interested in knowing how it went 👀
Hey! This is still ongoing. We have the pipeline up and running but we are having a hard time to finetune correctly a spk emb model on our smallish dataset 😿
Is your feature request related to a problem? Please describe. The title is pretty self-explanatory. I'd just like to know how much work would be needed to implement a pipeline for a speaker unrolling task: are all the required building blocks already here in your opinion? If there isn't too much digging involved, i'd probably be willing to do it myself :)