Xiaojie Yu

Xiaoxu Zhu, Junhua Li, Aaron J. Li et al.

Self-supervised speech models learn representations that capture both content and speaker information. Yet this entanglement creates problems: content tasks suffer from speaker bias, and privacy concerns arise when speaker identity leaks through supposedly anonymized representations. We present two contributions to address these challenges. First, we develop InterpTRQE-SptME (Timbre Residual Quantitative Evaluation Benchmark of Speech pre-training Models Encoding via Interpretability), a benchmark that directly measures residual speaker information in content embeddings using SHAP-based interpretability analysis. Unlike existing indirect metrics, our approach quantifies the exact proportion of speaker information remaining after disentanglement. Second, we propose InterpTF-SptME, which uses these interpretability insights to filter speaker information from embeddings. Testing on VCTK with seven models including HuBERT, WavLM, and ContentVec, we find that SHAP Noise filtering reduces speaker residuals from 18.05% to nearly zero while maintaining recognition accuracy (CTC loss increase under 1%). The method is model-agnostic and requires no retraining.

Xiaojie Yu, Haibo Zhang, Lizhi Peng et al.

ReduNet is a deep neural network model that leverages the principle of maximal coding rate \textbf{redu}ction to transform original data samples into a low-dimensional, linear discriminative feature representation. Unlike traditional deep learning frameworks, ReduNet constructs its parameters explicitly layer by layer, with each layer's parameters derived based on the features transformed from the preceding layer. Rather than directly using labels, ReduNet uses the similarity between each category's spanned subspace and the data samples for feature updates at each layer. This may lead to features being updated in the wrong direction, impairing the correct construction of network parameters and reducing the network's convergence speed. To address this issue, based on the geometric interpretation of the network parameters, this paper presents ESS-ReduNet to enhance the separability of each category's subspace by dynamically controlling the expansion of the overall spanned space of the samples. Meanwhile, label knowledge is incorporated with Bayesian inference to encourage the decoupling of subspaces. Finally, stability, as assessed by the condition number, serves as an auxiliary criterion for halting training. Experiments on the ESR, HAR, Covertype, and Gas datasets demonstrate that ESS-ReduNet achieves more than 10x improvement in convergence compared to ReduNet. Notably, on the ESR dataset, the features transformed by ESS-ReduNet achieve a 47\% improvement in SVM classification accuracy.