AI-powered speech recognition systems have made great progress in recent years, with speech-to-text processing now so powerful that the occasional errors are little more than curious exceptions. Most contemporary models addressing this task however require massive labelled training data -- which is simple enough to source for English, Chinese, and other popular languages but challenging to obtain in the case of the low-resource tongues that make up the majority of the world's 8,000 languages. To address this issue, a Carnegie Mellon University research team has developed a speech recognition pipeline that can recognize 1909 languages without any audio for the target language. Their ASR2K pipeline achieves impressive 45 percent CER and 69 percent WER scores when using 10,000 raw text utterances on the CMU Wilderness dataset, and is introduced in the paper ASR2K: Speech Recognition for Around 2000 Languages Without Audio. The proposed pipeline comprises separate acoustic, pronunciation, and language models.