LID rises under low-SNR perturbations in models like WavLM and wav2vec 2.0, diverges between benign and adversarial noise at high SNR, co-occurs with higher WER, and supports anomaly detection at AUROC 0.78-1.00.
Extreme-value-theoretic estima- tion of local intrinsic dimensionality,
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Dimensionality-Aware Anomaly Detection in Learned Representations of Self-Supervised Speech Models
LID rises under low-SNR perturbations in models like WavLM and wav2vec 2.0, diverges between benign and adversarial noise at high SNR, co-occurs with higher WER, and supports anomaly detection at AUROC 0.78-1.00.