Using Data Augmentations and VTLN to Reduce Bias in Dutch End-to-End
  Speech Recognition Systems
Tanvina Patel Odette Scharenborg
Speech technology has improved greatly for norm speakers, i.e., adult native
speakers of a language without speech impediments or strong accents. However,
non-norm or diverse speaker groups show a distinct performance gap with norm
speakers, which we refer to as bias. In this work, we aim to reduce bias
against different age groups and non-native speakers of Dutch. For an
end-to-end (E2E) ASR system, we use state-of-the-art speed perturbation and
spectral augmentation as data augmentation techniques and explore Vocal Tract
Length Normalization (VTLN) to normalise for spectral differences due to
differences in anatomy. The combination of data augmentation and VTLN reduced
the average WER and bias across various diverse speaker groups by 6.9% and
3.9%, respectively. The VTLN model trained on Dutch was also effective in
improving performance of Mandarin Chinese child speech, thus, showing
generalisability across languages