Voice interfaces often struggle with specific types of named content. Domain-specific terminology and naming may push the bounds of standard language, especially in domains like music where artistic creativity extends beyond the music itself. Artists may name themselves with symbols (e.g. M S C RA) that most standard automatic speech recognition (ASR) systems cannot transcribe. Voice interfaces also experience difficulty surfacing content whose titles include non-standard spellings, symbols or other ASCII characters in place of English letters, or are written using a non-standard dialect. We present a generalizable method to detect content that current voice interfaces underserve by leveraging differences in engagement across input modalities. Using this detection method, we develop a typology of content types and linguistic practices that can make content hard to surface. Finally, we present a process using crowdsourced annotations to make underserved content more accessible.