Comments (4)
In the studies that we have conducted so far (for both speaker and speech recognition) we have done training and test on the same dataset (as often done in the speech/speaker recognition community). We didn't check so far cross-dataset performance. I think the behavior is similar to that of standard neural networks: if the datasets A and B are similar you might have good performance, otherwise, you likely have a performance drop. We are currently working on some adaptation strategies (that will probably be an object of a future paper) in order to perform a quick unsupervised adaptation of SincNet to conditions very different from that seen during training. The big advantage of SincNet if that the first convolutional layers is based on a few hundreds of learnable parameters only, while standard CNNs are based on thousands of them. This feature could make adaptation much easier!
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alright, hope you successfully.
from sincnet.
I appreciated your sinc_conv.
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nevertheless, sentence embedding a little more in SincNet.forward in dnn_models.py
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Related Issues (20)
- Hamming window simplification
- the problem with the other dataset
- loss function returns nan
- Need some idea on how to use this model to perform speaker verification task.
- DownStream (ASV: Automatic Speaker Verification) - missing information
- Question about the creation of the Sinc filterbank
- Voxceleb1 cfg file?
- Sincnet show the spectrogram is so odd ,it create so many stripe,why???? HOT 1
- CUDA error: device-side assert triggered
- .scp files for VoxCeleb1/VoxCeleb2 - how to get/generate them
- Clarifications for creating sequential batches instead of random sampling.
- cw_len
- SincNet visualization
- Is the Learning Speaker Representations with Mutual Information paper ever going to be implemented?
- How long did it take to run the Timit dataset? HOT 1
- chunk numbers in code "N_fr"
- Use of two optimizers?
- SincNet band
- Can it work on pure music?
- Difference between mfcc feature and torch tensor feature used while computing d vector? Does mfcc affect speaker identification accuracy?
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