Building on earlier discussions of computational approaches to music analysis, recent developments in artificial intelligence invite renewed reflection on how technologies reshape listening and musical knowledge. Drawing on Thomas Porcello’s concept of techoustemology (2004), this study examines how AI-assisted analysis reconfigures modes of listening, analytical practice, and knowledge production in the study of historical recordings, taking Taiwanese Hakka shangezi folksongs from commercially published recordings of the 1960s as a case study. Using AI-assisted tools for vocal–instrument separation, pitch contour extraction, and phoneme-level alignment, the study conducts a detailed examination of singers’ pronunciation and melodic realization at the syllabic level. While shangezi performance is conventionally expected to adhere to a standardized Sixian-accented Hakka, close analysis reveals that individual singers frequently depart from such prescriptive linguistic norms. Variations in consonant and vowel articulation, as well as tonal realization, suggest a more fluid and multilingual vocal practice than the ideal of linguistic purity implies. Rather than presenting AI as a neutral analytical instrument, this study foregrounds how the technical requirements of segmentation and alignment intensify critical listening and detailed transcription. The analytical process does not automatically determine dialectal variation; instead, it creates conditions under which discrepancies between normative expectations and embodied performance become perceptible. AI-assisted analysis is thus proposed not as a replacement for ethnomusicological interpretation, but as a reflexive techoustemological method—one that recognizes technology as constitutive of how we hear, analyze, and understand historical performance practice.