  We propose a unified framework for low resource automatic speech recognition
tasks named meta audio concatenation (MAC). It is easy to implement and can be
carried out in extremely low resource environments. Mathematically, we give a
clear description of MAC framework from the perspective of bayesian sampling.
In this framework, we leverage a novel concatenative synthesis text-to-speech
system to boost the low resource ASR task. By the concatenative synthesis
text-to-speech system, we can integrate language pronunciation rules and adjust
the TTS process. Furthermore, we propose a broad notion of meta audio set to
meet the modeling needs of different languages and different scenes when using
the system. Extensive experiments have demonstrated the great effectiveness of
MAC on low resource ASR tasks. For CTC greedy search, CTC prefix, attention,
and attention rescoring decode mode in Cantonese ASR task, Taiwanese ASR task,
and Japanese ASR task the MAC method can reduce the CER by more than 15\%.
Furthermore, in the ASR task, MAC beats wav2vec2 (with fine-tuning) on common
voice datasets of Cantonese and gets really competitive results on common voice
datasets of Taiwanese and Japanese. Among them, it is worth mentioning that we
achieve a \textbf{10.9\%} character error rate (CER) on the common voice
Cantonese ASR task, bringing about \textbf{30\%} relative improvement compared
to the wav2vec2 (with fine-tuning).
