From: NIPS.cc/2018/Workshop/IRASL
decision: Accept
title: Acceptance Decision

From: NIPS.cc/2018/Workshop/IRASL/Paper44/Authors
title: Thank you for the feedback and proof reading
comment: We will make all the changes that you spotted/suggested in the next version of the document.

Our apologies for the oversight regarding the citations, especially as the work of Ganin et. al and Tzeng et. al inspired our approach.

From: NIPS.cc/2018/Workshop/IRASL/Paper44/Authors
title: Thank you for your feedback
comment: My apologies for the oversight, especially as we use the gradient reversal framework proposed by Ganin et al in one of our models. The citation will be included in the next version of the document.

- Why is the encoder called a "generator"?  
As the idea of using an adversarial loss to train the encoder is fairly new in the speaker verification community,  I was trying to make it clear that the encoder/feature extractor is playing a role analogous to a generator in a traditional GAN, i.e. trying to fool the discriminator. That being said, I agree that it can be confusing and the model is not generative. I will change this terminology in the next draft.

From: NIPS.cc/2018/Workshop/IRASL/Paper44/AnonReviewer1
title: GANs for domain adaptation in language-mismatched speaker recognition
review: In this paper, the authors evaluate several GAN variants for text-independent speaker verification and focus on the domain mismatch problem. A new objective function is proposed for the generator network and has been proven efficient without needing any of the discriminative scoring models usually used with i-vectors/x-vectors (LDA,PLDA). This work is a good step towards the development of end-to-end speaker verification system in language-mismatched conditions.

The paper is well written and well referenced. The experimental setup and analysis are easy to read. 

* In the paragraph following equation 3 (subsection 2.2  : self-attentive speaker statistics) : nBXnFxT is hard to read ; putting B and F as subscript n_{B} , n_{F} and adding a multiplication sign between the terms (\times) instead of (x and X) might help.

* Same for 3X23 in section 4, please use the standard latex multiplication symbol (\times) to avoid any confusion.

* "." missing at the end of the sentence following equation 6 (section 3) and at the end of all table and figure captions. 

* The right parenthesis of the discriminator is missing in all equations where D(.) is used (equation 5,7,8). The right parenthesis of the log is also missing in equation 5 : log(1-D(E(xt) --> log(1-D(E(xt)))

* Please check the parentheses in the exponent of the first term in equation 4 (subsection 2.3 Classifier).

* For consistency (with figure 1), please use "FC3" instead of "fc3" in the paragraph "speaker verification" in section 4.

* In the "Training Data" paragraph of section 4 (Experimental setup): 
- a reference is missing in the third line : noise and reverberation, as in [].
- the tilde symbol should be placed before the 220k.

* Please add the evaluation metric to the caption of Table 1 (EER) 

* In the paragraph "Source domain speaker verification" lower MMD and fr'echet distances --> lower MMD and fréchet distances

* In the results section : Tables 1 & 2 compares --> Tables 1 & 2 compare

* Please define DANSE 

* It would be better to add some references for domain adaptation using GANs since this approach has been used for other applications (work of : Yaroslav Ganin, Eric Tzeng, ..)

* Regarding the sampling procedure : it might be better to add the real amount of training data used to train the system after the sampling procedure (either in % or in duration). This information can be helpful for future replication of the results. 



rating: 3: Marginally above acceptance threshold
confidence: 3: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature

From: NIPS.cc/2018/Workshop/IRASL/Paper44/AnonReviewer3
title: GANs for Speaker Recognition 
review: In this work, authors explored a number of GAN variants on the task of speaker recognition in the domain mismatched condition. The GAN is learned using an end-to-end loss function by eliminating the need for a separate classifier (e.g. PLDA) in speaker recognition.

The evaluation is performed on NIST SRE 2016 set representing a domain mismatch between training and test data. However, the proposed approach doesn't outperform the x-vector system with LDA/PLDA backend it is a good first step towards the development of end-to-end speaker recognition system. 

Authors also provide an analysis of their proposed auxGAN with different GAN varients in section 6. 

Minor comments:
Sec 1 
- space before Arguably 
- a that ---> that a 

Sec 2.1
- or to our

Sec 3
- Equation (3) represents the most general form ..... should be Equation (6)

Sec 4
- The first paragraph is missing a reference []
- Define DANSE 
- The last line should be  EER in percentage. 


rating: 3: Marginally above acceptance threshold
confidence: 3: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature

From: NIPS.cc/2018/Workshop/IRASL/Paper44/AnonReviewer2
title: Using adversarial domain adaptation for speaker identification
review: This method is motivated by problems where the labeled data contains speakers with speech patterns which differ from those which appear in the test set.  For example, in some datasets the languages seen in the evaluation set are different from those seen in the training set.  The NIST-SRE dataset, for example, includes these discrepancies.  

The model involves a feature extractor, followed by a cross-entropy loss for speaker classification, as well as a small fully-connected domain classifier.  

The method seems correct and the improvements also seem significant.  

Issues: 
  -The idea of adversarial domain adaptation is already published (see Yaroslav Ganin's work) - this isn't cited at all.  It should actually be cited quite prominently.  

  -Using a discriminator on encoded features also has a lively history of research.  For example Adversarially Learned Inference (Dumoulin 2016).  

  -Why is the encoder called a "generator"?  
rating: 3: Marginally above acceptance threshold
confidence: 2: The reviewer is fairly confident that the evaluation is correct