  Building inclusive speech recognition systems is a crucial step towards
developing technologies that speakers of all language varieties can use.
Therefore, ASR systems must work for everybody independently of the way they
speak. To accomplish this goal, there should be available data sets
representing language varieties, and also an understanding of model
configuration that is the most helpful in achieving robust understanding of all
types of speech. However, there are not enough data sets for accented speech,
and for the ones that are already available, more training approaches need to
be explored to improve the quality of accented speech recognition. In this
paper, we discuss recent progress towards developing more inclusive ASR
systems, namely, the importance of building new data sets representing
linguistic diversity, and exploring novel training approaches to improve
performance for all users. We address recent directions within benchmarking ASR
systems for accented speech, measure the effects of wav2vec 2.0 pre-training on
accented speech recognition, and highlight corpora relevant for diverse ASR
evaluations.
