Recent work has shown that it is possible to train an $\textit{unsupervised}$
automatic speech recognition (ASR) system using only unpaired audio and text.
Existing unsupervised ASR methods assume that no labeled data can be used for
training. We argue that even if one does not have any labeled audio for a given
language, there is $\textit{always}$ labeled data available for other
languages. We show that it is possible to use character-level acoustic models
(AMs) from other languages to bootstrap an $\textit{unsupervised}$ AM in a new
language. Here, "unsupervised" means no labeled audio is available for the
$\textit{target}$ language. Our approach is based on two key ingredients: (i)
generating pseudo-labels (PLs) of the $\textit{target}$ language using some
$\textit{other}$ language AM and (ii) constraining these PLs with a
$\textit{target language model}$. Our approach is effective on Common Voice:
e.g. transfer of English AM to Swahili achieves 18% WER. It also outperforms
character-based wav2vec-U 2.0 by 15% absolute WER on LJSpeech with 800h of
labeled German data instead of 60k hours of unlabeled English data.

研究表明，在没有成对的语音和文本的情况下，可以使用其他语言的字符级声学模型引导新语言的无监督自动语音识别系统，方法基于两个主要组成部分：使用其他语言 AM 生成目标语言的伪标签并用目标语言模型加以约束。