Editing State-of-the-art (section)

==== Introduction ====
More often than not, speech corpora either contain only native speech, or the non-native subset is significantly underrepresented. At the same time, gender and foreign accent are the most salient factors contributing to changes in the acoustics of speech. However, not only are there numerous possible combinations of L1 and L2s, but the annotation and labelling os recordings to a suitable degree (e.g. age of L2 acquisition, country of origin, L1, L2 proficiency, language of education etc. are all factors that should be reported in order to make the speech resources reliable and usable) are laborious and expensive.

In light of these challenges, methods of synthetical data augmentation have been recently explored in the literature. While creating synthetically-accented data through accent conversion models (ACMs) is a straightforward, inexpensive, and oof-the-shelf approach, it is not without limitations and the degree to which recognition performance is improved through such approaches depends on several factors. The following three articles provide some insight into these approaches and highlight both major advantages and persistent challenges.