This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, corresponding to the paper Fully Supervised Speaker Diarization.
In my experiment, I find the speaker number predicted by uis-rnn model is more than real number.
I want to know how to control the different speaker number predicted by uis-rnn model.
Which parameters in inference stage can do it?
My background
Have I read the README.md file?
yes/no - if you answered no, please stop filing the issue, and read it first
Have I searched for similar questions from closed issues?
yes/no - if you answered no, please do it first
Have I tried to find the answers in the paper Fully Supervised Speaker Diarization?
yes/no
Have I tried to find the answers in the reference Speaker Diarization with LSTM?
yes/no
Have I tried to find the answers in the reference Generalized End-to-End Loss for Speaker Verification?
Describe the question
In my experiment, I find the speaker number predicted by uis-rnn model is more than real number. I want to know how to control the different speaker number predicted by uis-rnn model. Which parameters in inference stage can do it?
My background
Have I read the
README.md
file?Have I searched for similar questions from closed issues?
Have I tried to find the answers in the paper Fully Supervised Speaker Diarization?
Have I tried to find the answers in the reference Speaker Diarization with LSTM?
Have I tried to find the answers in the reference Generalized End-to-End Loss for Speaker Verification?