Speech translation for low-resource languages is constrained by the scarcity of high-quality parallel speech data, which is particularly pronounced in African linguistic contexts. To address this, we introduce , a parallel speech translation dataset covering Igbo, Hausa, , and Nigerian Pidgin paired with English. The dataset contains more than 40 hours of speech per language and includes several speaker and accent variations to reflect realistic settings. Using NaijaS2ST, we benchmark cascaded, end-to-end (E2E), and AudioLLM-based approaches across bidirectional translation directions (XX↔English). Our results show that few-shot audio LLMs are more effective than cascaded and E2E methods finetuned for speech-to-text translation. For speech-to-speech translation, however, cascaded and audio LLM paradigms perform comparatively, highlighting the need for stronger task-specific models. offers a strong foundation towards advancing research on low-resource multilingual speech translation. We open source the dataset here: [NaijaS2ST Huggingface Dataset](https://huggingface.co/datasets/McGill-NLP/NaijaS2ST); and the code here: [NaijaS2ST Code](https://github.com/McGill-NLP/naijas2st).

Code-switching is a widespread practice among the world’s multilingual majority, yet few benchmarks accurately reflect its complexity in everyday communication. We present PINGPONG, a benchmark for natural multi-party code-switching dialogues covering five language-combination variations, some of which are trilingual. Our dataset consists of human-authored conversations among 2 to 4 participants covering authentic, multi-threaded structures where replies frequently reference much earlier points in the dialogue. We demonstrate that our data is significantly more natural and structurally diverse than machine-generated alternatives, offering greater variation in message length, speaker dominance, and reply distance. Based on these dialogues, we define three downstream tasks: Question Answering, Dialogue Summarization, and Topic Classification. Evaluations of several state-of-the-art language models on PINGPONG reveal that performance remains limited on code-switched inputs, underscoring the urgent need for more robust NLP systems capable of addressing the intricacies of real-world multilingual discourse.