Monosemantic Whisper SAE latents encode phonetic/lexical features and steer transcripts

Pluth et al. train a TopK sparse autoencoder on ~200M frames of Whisper-base encoder activations, recovering monosemantic dictionary directions [pluth-etal-2026-mechanistic-interpretability-asr-sae] These span diphone (e.g. /r-uw/ precision 88.7%), word (e.g. "his" precision 99.3%), language-discrimination (recall 91.2%), and profanity (recall 89.7%) features [pluth-etal-2026-mechanistic-interpretability-asr-sae] Adding or clamping these SAE decoder directions causally steers Whisper's transcript content, including cross-lingually [pluth-etal-2026-mechanistic-interpretability-asr-sae]