Most recent speech recognition models rely on large supervised datasets,
which are unavailable for many low-resource languages. In this work, we present
a speech recognition pipeline that does not require any audio for the target
language. The only assumption is that we have access to raw text datasets or a
set of n-gram statistics. Our speech pipeline consists of three components:
acoustic, pronunciation, and language models. Unlike the standard pipeline, our
acoustic and pronunciation models use multilingual models without any
supervision. The language model is built using n-gram statistics or the raw
text dataset. We build speech recognition for 1909 languages by combining it
with Crubadan: a large endangered languages n-gram database. Furthermore, we
test our approach on 129 languages across two datasets: Common Voice and CMU
Wilderness dataset. We achieve 50% CER and 74% WER on the Wilderness dataset
with Crubadan statistics only and improve them to 45% CER and 69% WER when
using 10000 raw text utterances.

提出了一种语音识别流程，可以利用 n-gram 统计信息或原始文本数据集，在没有音频文件的情况下，使用多语种模型构建语音识别流程，并在 1909 种语言上进行了测试。