  Speech synthesis (text to speech, TTS) and recognition (automatic speech
recognition, ASR) are important speech tasks, and require a large amount of
text and speech pairs for model training. However, there are more than 6,000
languages in the world and most languages are lack of speech training data,
which poses significant challenges when building TTS and ASR systems for
extremely low-resource languages. In this paper, we develop LRSpeech, a TTS and
ASR system under the extremely low-resource setting, which can support rare
languages with low data cost. LRSpeech consists of three key techniques: 1)
pre-training on rich-resource languages and fine-tuning on low-resource
languages; 2) dual transformation between TTS and ASR to iteratively boost the
accuracy of each other; 3) knowledge distillation to customize the TTS model on
a high-quality target-speaker voice and improve the ASR model on multiple
voices. We conduct experiments on an experimental language (English) and a
truly low-resource language (Lithuanian) to verify the effectiveness of
LRSpeech. Experimental results show that LRSpeech 1) achieves high quality for
TTS in terms of both intelligibility (more than 98% intelligibility rate) and
naturalness (above 3.5 mean opinion score (MOS)) of the synthesized speech,
which satisfy the requirements for industrial deployment, 2) achieves promising
recognition accuracy for ASR, and 3) last but not least, uses extremely
low-resource training data. We also conduct comprehensive analyses on LRSpeech
with different amounts of data resources, and provide valuable insights and
guidances for industrial deployment. We are currently deploying LRSpeech into a
commercialized cloud speech service to support TTS on more rare languages.
