Editing State-of-the-art (section)

==== Do, P., Coler, M., Dijkstra, J., & Klabbers, E. (2021). A Systematic Review and Analysis of Multilingual Data Strategies in Text-to-Speech for Low-Resource Languages. Proc. Interspeech 2021, 16–20. doi: 10.21437/Interspeech.2021-1565 ====

* Summary: The article provides an overview of strategies for test-to-speech for low-resource langauges, focusing on Multilingual Data strategies. More specifically, this article presents an evaluation of the results of the previous studies on LRLs TTS, an evaluation of the influence of data augmentation techniques employed on the performance of the models and the proposal of a new measure to evaluate the performance of multilingual vs. monolingual systems with different evaluation metrics, namely MultiLingual Model Effect (''MLME''). The performance of the strategies analysed is also checked by verifying how different factors influence it.
* RQ: 
*# Using the same limited amount of LRL data, how does the output quality of multilingual TTS models compare to that of monolingual models?
*# What factors in the data augmentation strategy influence the effect of using multilingual TTS models on output quality, and to what extent do they affect it?
* Hypothesis: Looking at the correlations between data augmentation strategies and synthesized speech quality, tools that use multilingual data can be provided for future research in TTS for LRLs, especially regarding the efficiency of using such data.
* Conclusion: Multilingual approaches are more effective in training for LRLs. The factors that affect the performance are: 
** target language data ratio between corresponding multilingual and monolingual models;
** target language data balance ratio over total training data
** amount of target language data.
* Critical observations: The paper only focuses on multilingual data strategies, and justifies the choice by saying that multispeaker data are harder to collect for LRLs. Even though I understand the reasoning behind this, I believe this is not entirely true. On one hand, it is true indeed that it is harder to find many speakers for a LRLs, since oftentimes such languages are also minority languages. On the other hand, collecting multispeaker data means that each speaker can contribute with a very small amount of data and still get enough of them. This means that by adopting multispeaker TTS techniques, we don't need to record one speaker for a long time, but rather multiple speakers for a short time. This multi-speaker approach, I believe, could be used in combination with Transfer Learning to improve the results of LRLs TTS systems, even though this implies adding complezity to the pipeline.
* Relevance: The most relevant outcome of this study, especially for LRLs TTS, is that the '''''language family is not relevant for the selection of the target-source language pair''', no matter the architecture.'' In my opinion, the conclusions of this paper are also relevant for medium-resourced languages and in general for the synthesis of non-standard speech and for all the types of speech that are not widely covered by the research so far.