Sparse-autoencoder decompositions of real neural audio codecs encode accent in activation magnitude or position, depending on codec type

Wang, Feng, Kommineni, Lertpetchpun, Yi, Shi & Narayanan (2026) train TopK sparse autoencoders (latent ratio and sparsity swept) on mean-pooled utterance-level representations of four real neural audio codecs (EnCodec, DAC, SpeechTokenizer, Mimi), then fit logistic-regression probes on the full sparse code, on its position-only pattern, and on its magnitude-only pattern for binary accent classification (US vs. UK; US vs. non-US/UK) [wang-etal-2026-towards-interpretable-framework-for-neural-audio-codecs-via-sparse-autoencoders-accent] For DAC (acoustic-oriented), magnitude-only features preserve more accent-predictive information than position-only (DeltaF1 ~-5.0 to -5.1% vs. ~-9.8 to -12.0% at 5% sparsity); for SpeechTokenizer (phonetic-oriented, distilled from HuBERT/WavLM), the pattern reverses -- position-only preserves more than magnitude-only (~-4.5 to -6.3% vs. ~-7.1 to -7.2%) [wang-etal-2026-towards-interpretable-framework-for-neural-audio-codecs-via-sparse-autoencoders-accent] Reference (dense-representation) accent classification F1 ranges from 79.7-92.4% (US-vs-UK) across the four codecs; purely correlational, with feature steering explicitly named as future work in the paper's own conclusion [wang-etal-2026-towards-interpretable-framework-for-neural-audio-codecs-via-sparse-autoencoders-accent]