Automatic speech translation (AST) promotes seamless communication among speakers of different languages. While current state-of-the-art models excel with high-resource languages, their performance on low-resource languages (LRLs) is not well-established. We investigate this by evaluating state-of-the-art models on 10 LRLs with varying data amounts (10-30+ hours). Through six finetuning strategies and experimenting with three main AST paradigms, we observe that: (1) The latest Large Language Models (LLMs) may struggle with LRLs. (2) Comprehensive experiments suggest that for LRLs, more AST finetuning data is not always beneficial. (3) Our 2-Stage with ASR corrector finetuning recipe can substantially improve AST performance on LRLs, achieving up to a 5.8x BLEU score boost on translating related languages to English, while on par with the best monolingual finetuning in BLEU score when translating the target language to English. (4) We share effective engineering practices, including how to effectively adapt AST models to unseen languages.

Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) — languages for which NLP research is particularly far behind in meeting user needs — it is feasible to annotate small amounts of data. Motivated by this, we propose XTREME-UP, a benchmark defined by: its focus on the scarce-data scenario rather than zero-shot; its focus on user-centric tasks — tasks with broad adoption by speakers of high-resource languages; and its focus on under-represented languages where this scarce-data scenario tends to be most realistic. XTREME-UP evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks that are of general utility. We create new datasets for OCR, autocomplete, semantic parsing, and transliteration, and build on and refine existing datasets for other tasks. XTREME-UP provides methodology for evaluating many modeling scenarios including text only, multi-modal (vision, audio, and text), supervised parameter tuning, and in-context learning. We evaluate commonly used models on the benchmark. We release all code and scripts to train and evaluate models.