  The mainstream automatic speech recognition (ASR) technology usually requires
hundreds to thousands of hours of annotated speech data. Three approaches to
low-resourced ASR are phoneme or subword based supervised pre-training, and
self-supervised pre-training over multilingual data. The Iu Mien language is
the main ethnic language of the Yao ethnic group in China and is low-resourced
in the sense that the annotated speech is very limited. With less than 10 hours
of transcribed Iu Mien language, this paper investigates and compares the three
approaches for Iu Mien speech recognition. Our experiments are based on the
recently released, three backbone models pretrained over the 10 languages from
the CommonVoice dataset (CV-Lang10), which correspond to the three approaches
for low-resourced ASR. It is found that phoneme supervision can achieve better
results compared to subword supervision and self-supervision, thereby providing
higher data-efficiency. Particularly, the Whistle models, i.e., obtained by the
weakly-supervised phoneme-based multilingual pre-training, obtain the most
competitive results.
