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

ASR2K: Speech Recognition for Around 2000 Languages without Audio