Voice conversion (VC) could be used to improve speech recognition systems in low-resource languages by using it to augment limited training data. However, VC has not been widely used for this purpose because of practical issues such as compute speed and limitations when converting to and from unseen speakers. Moreover, it is still unclear whether a VC model trained on one well-resourced language can be applied to speech from another low-resource language for the aim of data augmentation. In this work we assess whether a VC system can be used cross-lingually to improve low-resource speech recognition. We combine several recent techniques to design and train a practical VC system in English, and then use this system to augment data for training speech recognition models in several low-resource languages. When using a sensible amount of VC augmented data, speech recognition performance is improved in all four low-resource languages considered. We also show that VC-based augmentation is superior to SpecAugment (a widely used signal processing augmentation method) in the low-resource languages considered.
Cite as: Baas, M., Kamper, H. (2022) Voice Conversion Can Improve ASR in Very Low-Resource Settings. Proc. Interspeech 2022, 3513-3517, doi: 10.21437/Interspeech.2022-112
@inproceedings{baas22_interspeech,
author={Matthew Baas and Herman Kamper},
title={{Voice Conversion Can Improve ASR in Very Low-Resource Settings}},
year=2022,
booktitle={Proc. Interspeech 2022},
pages={3513--3517},
doi={10.21437/Interspeech.2022-112},
issn={2308-457X}
}