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

From: NIPS.cc/2018/Workshop/IRASL/Paper56/AnonReviewer2
title: Interesting work, but is not well written 
review: The authors showed an interesting work of cross-lingual voice conversion with unparalleled training corpora.  They took advantage of FHVAE to learn the disentangled representation of phonetic context and speaker identity. 

The work itself could bring up some insights of voice conversion with unparalleled cross-lingual corpora training. However, the paper is not well written and the experiment section is not thorough or convincing:

- 1.  Figures 2, 3 are lack of explanations;
- 2.  The legend in Figure 4 is confusing and Figure 5 has nothing to do with the language;
- 3.  It's not clear enough that why language embedding is important.

The paper has to be revised and needs more experiments to strengthen the arguments that the authors claimed in the abstract.
rating: 2: Marginally below acceptance threshold
confidence: 2: The reviewer is fairly confident that the evaluation is correct

From: NIPS.cc/2018/Workshop/IRASL/Paper56/AnonReviewer3
title: Could be interesting, but needs substantial revision
review: The authors apply the Factorized Hierarchical Variational Autoencoder (FHVAE)
for cross-lingual voice conversion. A one-hot representation is used in some
cases to help the model determine the input language.

Strengths:

The paper could provide (with some revisions) valuable empirical observations
about the use of FHVAE for cross-lingual voice conversion.

Weaknesses:

The task (cross-lingual voice conversion) should be described in more detail.

There is little technical novelty.

The writing quality is somewhat weak, with many ungrammatical passages
(e.g. "we decided to the second approach" (line 111), "we want the matrices
should be close" (line 153)).

Some corpora are proprietary, preventing easy replication of the experiments and
hindering future work by other groups.

The anonymity condition is broken (line 140).

Results are incomplete. In particular, Figure 4 doesn't match its description
in the text.

Minor remark: "i-vecotor" -> "i-vector" (line 48)

rating: 1: Strong rejection
confidence: 2: The reviewer is fairly confident that the evaluation is correct

From: NIPS.cc/2018/Workshop/IRASL/Paper56/AnonReviewer1
title: Paper tackles a challenging and interesting problem, but needs better presentation and convincing validation
review: This is an interesting approach for voice conversion. However, I don't find this paper ready for publication yet. The writing and presentation needs some work, but more importantly the evaluation is missing some important details and ultimately does not suggest that there's a statistically significant contribution here. Using an additional one-hot embedding is a very minor contribution for this workshop.



-- It's unclear from section 3 what aspect of the training forces  z_1 and z_2 to learn phonetic and speaker IDs respectively.
-- It's hard to appreciate from Figure 2 that conditioning on language ID brings the clusters closer to each other, as  claimed (seems to be true for some for some of the shapes on the plot, but not for others).
-- Figure 3 needs better labeling and caption (and/or explanation in the body) before we can decide how informative this is.  What are these 4 pairs of rows? How should we be looking at the figure to establish that  the matrices should be closer to each other, as claimed?
-- "The language breakdown is shown in Fig. 4" --- I see nothing about languages on Fig. 4

Evaluation:

-- Many more details needed in the evaluation section. What languages are being tested? If several languages, what are the demographics of the listeners (since the test was conducted in N. America)? Was nativeness information collected for the listeners? At least for the quality part of the evaluation this would seem rather important.. For the speaker ID part of the evaluation (which contains stimuli from  presents , do they speak/understand either language?

-- "We only report scores for "same" ..." -- Why?



Minor edits: The paper could use closer proof-reading for its English. There's a myriad of grammar and spelling issues. Here are some that jumped at me.

- an arbitrary sentences --> arbitrary sentences
- sexes --> gender
- known priori --> known a priori  (more than once)
- prior work focus --> prior work focuses
- i-vecotor --> i-vector



Notation:
- Label your equations!
- N_seg --> N_seq (line 87)
- Fig 2 caption: 'dot type" --> shape? -- Also, improve the caption. Colors stand for gender, each shape stands for a different language, and each (x,y) pair on the plot stands for ____ 


rating: 2: Marginally below acceptance threshold
confidence: 2: The reviewer is fairly confident that the evaluation is correct