Venue: Interspeech 2023 Workshop SSW

Reviews:

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: The paper argues for text instead of phoneme sequences as input into neural end-to-end speech synthesizers. The paper shows that the implicit G2P conversion in such a system can learn speaker-specific characteristics (such as sociophonetic and other variability) for different speakers. 
I am very much convinced that including G2P into an *end-to-end* system is very very plausible and the arguments of the paper are sound.
The (little?) draw backs of this paper are two-fold: there is no evaluation of the TTS results (naturalness, ...) as compared to a baseline approach. This is aggravated by the fact that G2P as part of end-to-end TTS doesn't work well (as is openly discussed already in the abstract). Therefore, while it may be more *plausible* to include G2P into the NN model, it may still not be the correct choice for technical systems. The paper fails to quantify the cost of modeling speaker-specifics in terms of e.g. MOS. 

All in all, I like the paper as I see it raising an important discussion point.
review_title: review

final_score: rating: 6: Marginally above acceptance threshold
review_content: This paper positions that the use of text (an even more end-to-end) representation can reveal ever more fully than letter-to-sound (L2S) or grapheme-to-phoneme (G2P) systems given enough data, reasonable alignment.  Moreover, the paper suggests that this direction should be pursued specifically to model those speech variants where prescriptive L2S models are incorrect -- including non-native speech,  dialects, and specifically, sociophonetic variation.

The position is, for the most part, well justified and an interesting counter argument to the position that holds that end-to-end models are best suited to capturing the "head" of the linguistic distribution and suffer when it comes to modeling "interesting" less represented phenomena, of the sort described here.

The constructive proposal is to "add a phone predictor to state-of-the-art end-to-end TTS systems in order to train their text encoder to accept text input".  Isn't this, to some extent, simply a retraining of existing L2S rules to better specific phenomena, rather than moving to the fully e2e paradigm and letting the data and model learn appropriate L2S mappings under appropriate sociophonetic conditions?

One limitation of this work is the empirical evaluation of this work is fairly narrow, focusing on a few well understood phenomena.  This leaves and open question if there is some sociolinguisic/sociophonetic phenomena that is not as well documented and understood, can this technique capture it, or reveal it, by virtue of training on text-input rather than using a L2S component?

In Section 3.4 it is implied that the input text is already normalized to some degree.  In Section 3.1.2, the model needs to predict target phones to operate.  To what extent do these components make the modeling more complicated or at least data-hungry, despite eliminating the L2S component of the modeling.  Specifically, the need for phonetic transcription of less-common phenomena is more challenging than re-using a L2S module.


Typo:
On page 2, Sections 2.2 and 3 letter-to-sound is abbreviated LTS, but in the abstract L2S is used. 
In Section 5, there is a missing space in "vs65%"
review_title: Interesting position and discussion

Meta-review: Dear authors,

SSW2003 received 45 papers. The acceptance rate is 82%. We are pleased to inform you that your paper has been ACCEPTED by the SSW2023 Programme Committee. 

Please read the reviews carefully and submit your camera-ready paper by June 28th. Most of reviewers performed a detailed review. Please answer to their questions and take into account their comments. Note that camera-ready papers are credited of one extra page to allow authors to consider reviewers’ suggestions. So max 7 pages in total including figures & refs.

Regards,
The SSW organizing chairs
Decision: Accept