  Speech to text models tend to be trained and evaluated against a single
target accent. This is especially true for English for which native speakers
from the United States became the main benchmark. In this work, we are going to
show how two simple methods: pre-trained embeddings and auxiliary
classification losses can improve the performance of ASR systems. We are
looking for upgrades as universal as possible and therefore we will explore
their impact on several models architectures and several languages.
