  We explore cross-lingual multi-speaker speech synthesis and cross-lingual
voice conversion applied to data augmentation for automatic speech recognition
(ASR) systems in low/medium-resource scenarios. Through extensive experiments,
we show that our approach permits the application of speech synthesis and voice
conversion to improve ASR systems using only one target-language speaker during
model training. We also managed to close the gap between ASR models trained
with synthesized versus human speech compared to other works that use many
speakers. Finally, we show that it is possible to obtain promising ASR training
results with our data augmentation method using only a single real speaker in a
target language.
