Weakness:
---Lack of novelty--- While the results are impressive (e.g., MOS score) I feel like the proposed approaches lack novelty. For example, 1)Duration prediction has been there for a while since Fastspeech1. 2)GaussianSampling is similar to EATS (https://arxiv.org/abs/2006.03575). 3)FVAE is also not proposed by the authors
---Lack of some experiments--- I assume the biggest advantage of Non-attentive Tacotron is the robustness in generation quality. If so, does the unsupervised Non-attentive Tacotron still has the advantages of less over-generation and word skipping problems? It’d have been nicer if the authors had shown UDR and WDR results in the unsupervised setting too.
---Writings--- Although using FVAE makes the unsupervised experiments successful, the insights on using FVAE to tackle this problem is not clearly stated. The authors must state why is combining FVAE approach expected to predict duration better than the naïve w/o FVAE version. Also, I ask the authors to keep readers in mind little more when writing the paper. The authors should make the writings clearer when explaining the existing methods. For example, in Section3, the depth of explanations on FVAE is too short. Please elaborate more on how it can be formulated as conditional VAE framework.
Rating: I consider it “not bad” paper, but I think the impact of this paper is not strong enough to pass the bar of ICLR because of the lack of novelty (as written in Weakness part). Therefore, I recommend rejection. However, I’d be happy to listen to the authors’ opinion regarding this issue. Questions:
--- Questions on Vanilla upsampling --- It has been shown by Fastspeech1 that the Vanilla upsampling gives similar score to Tacotron2 model (3.84 vs 3.86). In this paper, however, it seems like the Vanilla upsampling is not working very well compare to Tacotron2 (4.13 vs 4.37). I’d like to ask the authors what the source of this difference could be.
--- Questions on training WaveRNN --- The authors have written that WaveRNN model was trained on predicted features. In this case, I assume the ground truth waveform and the predicted features are unaligned because Tacotron2 autoregressively decodes features, which must be different to the ground truth mel-spectrogram. How could WaveRNN be trained well enough in this setting? Were the predicted features predicted using Teacher-forcing?
---Lack of novelty--- While the results are impressive (e.g., MOS score) I feel like the proposed approaches lack novelty. For example,