As automatic speech recognition (ASR) systems shift toward multilingual support and lowresource language modeling, phoneme-based layers serve as a critical language-agnostic foundation. However, most evaluations of ASR’s demographic biases related to race, age, gender, and accent focus on standard grapheme-based ASR systems with comparatively little emphasis on phoneme-based systems. In this study, we evaluate the performance of WhisperIPA and ZIPA, two state-ofthe-art open-source systems that generate International Phonetic Alphabet (IPA) transcriptions. Our evaluation includes existing multilingual speech corpora and demographically annotated English-language corpora, comparing modelgenerated IPA transcriptions against graphemeto-phoneme (G2P) systems using both standard phoneme error rate (PER) and a proposed Soft PER metric that tolerates linguistically similar phoneme substitutions. Our analysis examines how performance varies across language, gender, accent, ethnicity, and age, revealing persistent disparities even after accounting for acceptable phonemic variation. These findings, while limited, provide insight into potential sources of bias and inform the development of more inclusive and linguistically robust phonemebased ASR systems. Our code and data are publicly available.