arXiv:2609.10466v2 Announce Type: replace Abstract: First-language (L1) background classification from second-language English speech aims to identify pronunciation-related acoustic cues that remain informative across speakers and corpora. Global pooling is commonly used to aggregate frame-level speech representations, but averaging features across an utterance may obscure local acoustic differences in how individual phonemes are pronounced. To model these pronunciation-related cues before utterance-level aggregation, we introduce Phoneme-Aligned Input Design (PAID), which constructs acoustic units from frozen Whisper representations using transcript-assisted forced alignment. PAID applies a shared nonlinear mapping to each acoustic unit before utterance-level aggregation and incorporates auxiliary phoneme-label embeddings. We construct a six-L1 dataset with speaker-disjoint partitions and evaluate cross-corpus performance on the Speech Accent Archive (SAA). PAID achieves 89.72% Macro-F1 on the source Test set and 80.14% on SAA. Matched comparisons show that phoneme-aligned acoustic-unit modeling improves SAA Macro-F1 by 13.44 percentage points over global pooling. To address uneven classification performance across L1 backgrounds, we additionally evaluate class-sensitive training, which improves observed weakest-class performance.