Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 3: Weak Reject
review_content: This paper re-organized the high dimensional 1-D raw waveform as 2-D matrix. This method simulated the autoregressive flow. Log-likelihood could be calculated in parallel. Autoregressive flow was only run on row dimension. The number of required parameters was desirable to synthesize high-fidelity speech with the speed faster than real time. Although this method could not achieve top one in ranking in every measurements, the resulting performance was still obtained with the best average results. 

In general, this paper is clearly written, well organized and easy to follow. The authors carried out sufficient experiments and analyses, and proposed some rules of thumb to build a good model. On one hand, we may catch the contributions. But, on the other hand, the contributions were not clearly explained. The results were averaged but were not clearly explained.

The authors suggested to specify a bigger receptive field than the squeezed height. The property of getting better performance using deeper wavenet was "not" clearly explained and investigated. In the experiments, a small number of generative steps was considered. This is because short sequence based on autoregressive model was used. 

This paper mentioned that using convolution queue could improve the synthesis speed. But, the synthesis speed has been fast enough because it is almost 15 times faster than real time. In practical applications, 100x faster is almost the same as 15x faster for humans. But, the task isn’t interacted with human. It is suggested to focuse on reducing the number of parameters or enhancing the log likelihood.
review_title: Official Blind Review #2

final_score: rating: 6: Weak Accept
review_content: This submission belongs to the field of text-to-speech synthesis. In particular it looks at a novel way of formulating a normalising flow using 2D rather than conventional 1D representation. Such reformulation enables to provide interpretations to several existing approaches as well as formulate a new one with quite interesting properties. This submission would benefit from a discussion of limitations of your approach. 

I believe there is a great deal of interest in the use of normalising flows in the text-to-speech area. I believe this submission could be a good contribution to the area. The test log-likelihoods look comparable to existing approaches with significantly worse inference times. The mean opinion scores (MOS) seem to approach one of the standard baselines with significantly worse inference times though at the expense of increasing the number of model parameters from 6M to 86M parameters whilst gaining only 0.2 in MOS. The submission would have benefited from discussion about model complexity/expressivity and it's impact on MOS for WaveFlow, WaveNet and other approaches. 

The largest issues with this submission are:

1) lack of proper technical description of your model in sections 1 and 2 making reading sections 1,2,3,etc in order awkward. It seems the order should be 3,4,(5),1,2,(5). 
2) complete omission of conditioning on text to be synthesised; anyone not familiar deeply with speech synthesis will wonder where does the text come in
3) explicit statement of complexity for the operations involved using proper big-O notation; helps to avoid confusion about what do you mean by "parallel" (autoregressive WaveNet followed by parallel computation != parallel computation)

review_title: Official Blind Review #1

final_score: rating: 8: Accept
review_content: 
## Updated review

I have read the rebuttal. The new version of the paper is definitely clearer, especially the contribution section and the experimental results. The new version addresses all my concerns, hence I am upgrading my rating to Accept.

## Original review

This paper presents the WaveGlow model, a generative model for raw audio. The model is based on a 2D-matrix approach, which allows to generate the audio with a fixed amount of step. The model is shown to be a generalization of the two main approaches for raw audio generation, autoregressive flow and bipartite flow. The model is evaluated and compared with related work on an objective evaluation (Log-likelihood) and a subjective evaluation (MOS), and is shown to be a trade-off between memory footprint, generation speed and quality.

I think this paper should be accepted, for the following reasons:
- The theoretical framework presented is novel and significant, as it provides a unified view of the two main approaches for neural waveform generation.
- The experiments are reasonably convincing, although they could be improved.

Detailed comments:
- In the subjective evaluation section (5.2), Table 5 is hard to decipher, especially given that there are three measurements to take into account, so it's not easy to see the benefit of the approach. Maybe the results should be organised differently, for instance grouping them according to one measurement could help, typically showing what speed and MOS each of the three models can achieve for a given model size. Maybe plotting speed vs MOS for the same model size could also be interesting. 
- In the same section, is the WaveNet model the original one, or the Parallel WaveNet ? if it's the original, why not include Parallel WaveNet in the table ?
- Typo at the end of Section 1: "We orgnize" -> "organize"
review_title: Official Blind Review #3

Meta-review: The paper presented a unified framework for constructing likelihood-based generative models for raw audio. It demonstrated tradeoffs between memory footprint, generation speech and audio fidelity. The experimental justification with objective likelihood scores and subjective mean opinion scores are matching standard baselines. The main concern of this paper is the novelty and depth of the analysis. It could be much stronger if there're thorough analysis on the benefits and limitations of the unified approach and more insights on how to make the model much better. 
Decision: Reject