Yor\`ub\'a an African language with roughly 47 million speakers encompasses a
continuum with several dialects. Recent efforts to develop NLP technologies for
African languages have focused on their standard dialects, resulting in
disparities for dialects and varieties for which there are little to no
resources or tools. We take steps towards bridging this gap by introducing a
new high-quality parallel text and speech corpus YOR\`ULECT across three
domains and four regional Yor\`ub\'a dialects. To develop this corpus, we
engaged native speakers, travelling to communities where these dialects are
spoken, to collect text and speech data. Using our newly created corpus, we
conducted extensive experiments on (text) machine translation, automatic speech
recognition, and speech-to-text translation. Our results reveal substantial
performance disparities between standard Yor\`ub\'a and the other dialects
across all tasks. However, we also show that with dialect-adaptive finetuning,
we are able to narrow this gap. We believe our dataset and experimental
analysis will contribute greatly to developing NLP tools for Yor\`ub\'a and its
dialects, and potentially for other African languages, by improving our
understanding of existing challenges and offering a high-quality dataset for
further development. We release YOR\`ULECT dataset and models publicly under an
open license.

用高质量的平行文本和语音语料库 (YORÙLECT) 跨三个领域和四个地区的尼日利亚约鲁巴语方言开展研究实验，结果表明，标准尼日利亚约鲁巴语和其他方言在机器翻译、自动语音识别和语音到文本翻译等任务中存在明显的差异，在方言自适应微调的情况下，能够缩小这一差距，该研究为尼日利亚约鲁巴语及其方言以及其他非洲语言的自然语言处理工具的发展做出了重要贡献。