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Speech to text models tend to be trained and evaluated against a singletarget accent. This is especially true for English for which native speakersfrom the United States became the main benchmark. In this work, we are going toshow how two simple methods: pre-trained embeddings and auxiliaryclassification losses can improve the performance of ASR systems. We arelooking for upgrades as universal as possible and therefore we will exploretheir impact on several models architectures and several languages.

Pushing the performances of ASR models on English and Spanish accents