We propose a method for efficient field data collection of speech resource data which leverages short-form verbal arts, namely riddles and proverbs, which permit a predictable transcript to be assigned to naturalistic but conventionalized utterances. As a proof of concept, we describe a 5.25 hour corpus of proverbs and riddles collected for Kom, a low-resource language of Cameroon, and conduct ASR modeling experiments on the corpus. Results suggest that the method yields high quality speech data, albeit with relatively low lexical diversity. We highlight the alignment of the collected data with community priorities for cultural education and preservation in the Cameroonian context.