Automatic speech recognition (ASR) systems are known to be sensitive to the
sociolinguistic variability of speech data, in which gender plays a crucial
role. This can result in disparities in recognition accuracy between male and
female speakers, primarily due to the under-representation of the latter group
in the training data. While in the context of hybrid ASR models several
solutions have been proposed, the gender bias issue has not been explicitly
addressed in end-to-end neural architectures. To fill this gap, we propose a
data augmentation technique that manipulates the fundamental frequency (f0) and
formants. This technique reduces the data unbalance among genders by simulating
voices of the under-represented female speakers and increases the variability
within each gender group. Experiments on spontaneous English speech show that
our technique yields a relative WER improvement up to 9.87% for utterances by
female speakers, with larger gains for the least-represented f0 ranges.

通过使用一个数据增强技术，我们可以减轻性别偏见问题，在语音识别系统中模拟女性说话者的声音，增加各性别组内的变异性，并显著提高女性说话者的识别准确性。