This paper investigates how to reduce foreign accent in the synthesis of native (L1) speech for a non-native (L2) speaker. We focus on two major aspects of foreign accents: mispronunciations and improper prosody (rhythm, phonemes duration, and pauses). Firstly, to reduce mispronunciations, the mel-spectrograms generated by an L2 text-to-speech (TTS) model are fed to a pre-trained speech recognizer and the mispronunciation information is fed back to the TTS model during back-propagation to help the model learn correct native mel-spectrograms. Secondly, to imitate L1 speech prosody, a recent data augmentation (DA) technique originally proposed for speaking style transfer is applied to transfer L1 speaking style to L2 speakers. The DA technique creates additional L2 speeches when L2 speakers try to imitate L1 speeches. Automatic speech recognition on native-accented speeches synthesized from non-native speakers by the proposed method gives a lower word error rate. The speaker embeddings produced by a pre-trained speaker verifier from the original L2 speakers' speech and their synthesized speech are highly similar. Finally, subjective MOS scores on the synthesized speech show that they have good quality and reduced accentedness.
Cite as: Chung, R., Mak, B. (2022) Synthesizing Near Native-accented Speech for a Non-native Speaker by Imitating the Pronunciation and Prosody of a Native Speaker. Proc. Interspeech 2022, 4302-4306, doi: 10.21437/Interspeech.2022-11124
@inproceedings{chung22_interspeech,
author={Raymond Chung and Brian Mak},
title={{Synthesizing Near Native-accented Speech for a Non-native Speaker by Imitating the Pronunciation and Prosody of a Native Speaker}},
year=2022,
booktitle={Proc. Interspeech 2022},
pages={4302--4306},
doi={10.21437/Interspeech.2022-11124}
}