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We explore cross-lingual multi-speaker speech synthesis and cross-lingualvoice 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 voiceconversion to improve ASR systems using only one target-language speaker duringmodel training. We also managed to close the gap between ASR models trainedwith synthesized versus human speech compared to other works that use manyspeakers. Finally, we show that it is possible to obtain promising ASR trainingresults with our data augmentation method using only a single real speaker in atarget language.

ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversion