The digitization and computational processing of Holocaust testimony interviews are essential for the long-term preservation and accessibility of survivors’ narratives. However, automatic speech recognition (ASR) for Yiddish—the primary language of most Holocaust victims and survivors—remains underdeveloped. This paper introduces the first ASR system for European Yiddish, focused on the Northeastern (“Lithuanian”) dialect and trained on Holocaust survivor testimonies from the Corpus of Spoken Yiddish in Europe (42 hours of speech segments from 60 survivors). A systematic comparison of CTC-based ASR models using transcripts with different orthographic representations reveals that a Hebrew-based phonemic system with precomposed Unicode is optimal, achieving a mean WER of 37.96% compared to 59.40% WER for romanized Yiddish and 99.67% WER (catastrophic failure) for standard Yiddish spelled with decomposed Unicode. Cross-domain testing on Yiddish audiobooks provides additional support for a phonemic representation (27.07% WER, 6.56% CER). Together, the results suggest that automatic transcription developed from oral Holocaust testimonies can support further technological innovation in service of Yiddish-speaking communities.

The field of cultural NLP has recently experienced rapid growth, driven by a pressing need to ensure that language technologies are effective and safe across a pluralistic user base. This work has largely progressed without a shared conception of culture, instead choosing to rely on a wide array of cultural proxies. However, this leads to a number of recurring limitations: coarse national boundaries fail to capture nuanced differences that lay within them, limited coverage restricts datasets to only a subset of usually highly-represented cultures, and a lack of dynamicity results in static cultural benchmarks that do not change as culture evolves. In this position paper, we argue that these methodological limitations are symptomatic of a theoretical gap. We draw on a well-developed theory of culture from sociocultural linguistics to fill this gap by 1) demonstrating in a case study how it can clarify methodological constraints and affordances, 2) offering theoretically-motivated paths forward to achieving cultural competence, and 3) arguing that localization is a more useful framing for the goals of much current work in cultural NLP.