  Automatic speech recognition (ASR) should serve every speaker, not only the
majority ``standard'' speakers of a language. In order to build inclusive ASR,
mitigating the bias against speaker groups who speak in a ``non-standard'' or
``diverse'' way is crucial. We aim to mitigate the bias against
non-native-accented Flemish in a Flemish ASR system. Since this is a
low-resource problem, we investigate the optimal type of data augmentation,
i.e., speed/pitch perturbation, cross-lingual voice conversion-based methods,
and SpecAugment, applied to both native Flemish and non-native-accented
Flemish, for bias mitigation. The results showed that specific types of data
augmentation applied to both native and non-native-accented speech improve
non-native-accented ASR while applying data augmentation to the
non-native-accented speech is more conducive to bias reduction. Combining both
gave the largest bias reduction for human-machine interaction (HMI) as well as
read-type speech.
