Comments (11)
Thanks for your interest in the github!
I applied Sohn VAD to the speech corpus to generate labels.
The results of Sohn VAD are used as labels of the speech.
You can refer the below papers.
[1] Joint learning using denoising variational autoencoders for voice activity detection
[2] Boosting contextual information for deep neural network based voice activity detection
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Thanks a lot.
Can i have your contact information(E-mail and so on)?
Thanks
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You can contact me through "[email protected]"
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Okay, I have received your E-mail.
Thanks
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As I said above, we applied the Sohn VAD to each utterance.
Sohn VAD is the unsupervised learning-based method, so we do not need to train the model.
The results from the Sohn VAD are binary labels (1 for speech, 0 for non-speech).
Therefore, we can use this output as the label for the DNN-based VAD.
You can use any unsupervised learning-based VAD for the label generator.
Even a simple model can yield very good results in VAD for clean speech.
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There is no section that is related to generating labels.
I generated labels and features using matlab codes and uploaded the results in the github.
And I didn't include the matlab code in this github.
You have to write down the codes to generate labels.
There are many codes about unsupervised learning based VAD in the github.
In the matlab voicebox toolkit, there is Sohn VAD code or you can use the other VAD in the below.
http://cs.uef.fi/pages/tkinnu/VQVAD/VQVAD.zip.
The detailed explanation is in my previous answer.
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