arXiv subjects

Xianquan Jiang

Publications and source records attributed to Xianquan Jiang.

4 recordsLinked to original sources

arXiv2026

Adaptive Depth and Expert Refinement for Efficient Speech Enhancement

Most neural speech enhancement systems use a fixed processing depth for all inputs, which can introduce unnecessary computation when fewer refinement steps are sufficient. We propose Adaptive Depth and Expert Refinement (ADER), a parameter-shared progressive enhancement framework with input-dependent computation. ADER combines an Adaptive Depth Controller (ADC) for hard early termination with a Conditional Expert Router (CER) that selects one lightweight residual adapter at each executed refinement iteration. We further introduce Exit-aware Intermediate Supervision (EIS) to directly optimize candidate intermediate outputs for early exit. On VCTK-DEMAND, ADER reduces the parameter count and average computation of MP-SENet by 70.4% and 51.3%, respectively, while achieving a WB-PESQ of 3.37. Overall, ADER enables input-dependent refinement and reduces redundant computation during inference.

eess.AS↗

arXiv2026

Consensus-Guided Shared-Specific Tri-View Learning for Speech Emotion Recognition

Speech emotion recognition (SER) benefits from heterogeneous acoustic representations, but views derived from the same utterance contain both overlapping emotional evidence and representation-dependent cues. Direct fusion may therefore propagate redundant information or obscure complementary details. To address this issue, we propose Tri-view Consensus-Guided Fusion (TriCGF) for jointly modeling spectrogram, Mel-frequency cepstral coefficients, and HuBERT representations. TriCGF organizes each view into common and view-specific components before fusion. Cross-view Consensus Learning aggregates the common components into a global reference, while View-wise Gated Integration adaptively combines this reference with each view-specific component. A soft difference regularizer further discourages excessive information overlap. Under speaker-independent evaluation, TriCGF achieves 74.19% weighted accuracy (WA) and 75.17% unweighted accuracy (UA) on IEMOCAP, and 94.36% WA and 94.28% UA on EmoDB, outperforming representative SER methods on both datasets.

eess.AS↗

arXiv2026

A Lightweight Fourier-based Network for Binaural Speech Enhancement with Spatial Cue Preservation

Binaural speech enhancement faces a severe trade-off challenge, where state-of-the-art performance is achieved by computationally intensive architectures, while lightweight solutions often come at the cost of significant performance degradation. To bridge this gap, we propose the Global Adaptive Fourier Network (GAF-Net), a lightweight deep complex network that aims to establish a balance between performance and computational efficiency. The GAF-Net architecture consists of three components. First, a dual-feature encoder combining short-time Fourier transform and gammatone features enhances the robustness of acoustic representation. Second, a channel-independent globally adaptive Fourier modulator efficiently captures long-term temporal dependencies while preserving the spatial cues. Finally, a dynamic gating mechanism is implemented to reduce processing artifacts. Experimental results show that GAF-Net achieves competitive performance, particularly in terms of binaural cues (ILD and IPD error) and objective intelligibility (MBSTOI), with fewer parameters and computational cost. These results confirm that GAF-Net provides a feasible way to achieve high-fidelity binaural processing on resource-constrained devices.

eess.AS↗

arXiv2026

Behind the Scenes: Mechanistic Interpretability of LoRA-adapted Whisper for Speech Emotion Recognition

Large pre-trained speech models such as Whisper offer strong generalization but pose significant challenges for resource-efficient adaptation. Low-Rank Adaptation (LoRA) has become a popular parameter-efficient fine-tuning method, yet its underlying mechanisms in speech tasks remain poorly understood. In this work, we conduct the first systematic mechanistic interpretability study of LoRA within the Whisper encoder for speech emotion recognition (SER). Using a suite of analytical tools, including layer contribution probing, logit-lens inspection, and representational similarity via singular value decomposition (SVD) and centered kernel alignment (CKA), we reveal two key mechanisms: a delayed specialization process that preserves general features in early layers before consolidating task-specific information, and a forward alignment, backward differentiation dynamic between LoRA's matrices. Our findings clarify how LoRA reshapes encoder hierarchies, providing both empirical insights and a deeper mechanistic understanding for designing efficient and interpretable adaptation strategies in large speech models. Our code is available at https://github.com/harryporry77/Behind-the-Scenes.

cs.SD↗