<![CDATA[Background: Indonesia’s archipelagic linguistic ecology makes automatic speech recognition (ASR) a problem of sociotechnical representation, because spoken Indonesian is shaped by regional accent, local-language influence, code-switching, and uneven access to standardized speech resources. Objective: This study examines how Indonesian ASR resources, evaluation practices, and model documentation make regional and accent-varied voices differentially visible, testable, and interpretable. Method: This study applies a corpus-based ASR audit to 18 public and verifiable corpus items, combining archipelagic representation analysis, accent-sensitive error-evaluation readiness, and sociotechnical visibility assessment. Results: The corpus shows that regional-language resources are more visible than dialectal Indonesian speech directly suitable for accent-sensitive ASR evaluation. Error-evaluation readiness is uneven because several items require transcript alignment, utterance segmentation, metadata enrichment, or model-output comparison before reliable subgroup analysis can be conducted. Sociotechnical visibility is fragmented across direct speech evidence, preprocessing-dependent materials, literature-mediated findings, and model-opaque documentation. Implication: These findings reposition Indonesian ASR fairness as a question of audibility, evaluability, and linguistic justice rather than aggregate transcription accuracy alone. Novelty: This study contributes an archipelagic framework for assessing unequal machine listening in Indonesian ASR by integrating corpus representation, evaluation readiness, and sociotechnical visibility.]]>
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