The awareness for biased ASR datasets or models has increased notably in
recent years. Even for English, despite a vast amount of available training
data, systems perform worse for non-native speakers. In this work, we improve
an accent-conversion model (ACM) which transforms native US-English speech into
accented pronunciation. We include phonetic knowledge in the ACM training to
provide accurate feedback about how well certain pronunciation patterns were
recovered in the synthesized waveform. Furthermore, we investigate the
feasibility of learned accent representations instead of static embeddings.
Generated data was then used to train two state-of-the-art ASR systems. We
evaluated our approach on native and non-native English datasets and found that
synthetically accented data helped the ASR to better understand speech from
seen accents. This observation did not translate to unseen accents, and it was
not observed for a model that had been pre-trained exclusively with native
speech.

本研究旨在改进口音转换模型，通过加入声学知识，提高模型对不同口音发音的准确性，采用生成数据训练 ASR 系统，实验结果表明，合成带有口音的数据有助于提高 ASR 系统对已见口音的语音理解，但不能推广到未见口音及纯母语模型上。