  End-to-end models have gradually become the preferred option for automatic
speech recognition (ASR) applications. During the training of end-to-end ASR,
data augmentation is a quite effective technique for regularizing the neural
networks. This paper proposes a novel data augmentation technique based on
semantic transposition of the transcriptions via syntax rules for end-to-end
Mandarin ASR. Specifically, we first segment the transcriptions based on
part-of-speech tags. Then transposition strategies, such as placing the object
in front of the subject or swapping the subject and the object, are applied on
the segmented sentences. Finally, the acoustic features corresponding to the
transposed transcription are reassembled based on the audio-to-text
forced-alignment produced by a pre-trained ASR system. The combination of
original data and augmented one is used for training a new ASR system. The
experiments are conducted on the Transformer[2] and Conformer[3] based ASR. The
results show that the proposed method can give consistent performance gain to
the system. Augmentation related issues, such as comparison of different
strategies and ratios for data combination are also investigated.
