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

本研究采用速度扰动及谱增强的数据增强技术和 Vocal Tract Length Normalization 技术用于 end-to-end ASR 系统，旨在减少针对不同年龄组和非母语的荷兰语说话者的偏见，减少了平均 WER 和不同的说话人群之间的差异，同时该模型还适用于普通话儿童语音。