Conference on Empirical Methods in Natural Language Processing (EMNLP 2026), October 2026 Kuan-Tang Huang, Cheng-Yeh Yang, Chien-Chun Wang, Hung-Shin Lee, Hsin-Min Wang, and Berlin Chen Low-resource ASR remains difficult because scarce transcripts provide limited supervised evidence for target-side generation. To address this gap, we propose SAMA-ASR, a lightweight adapter mechanism that augments the decoder with semantic anchors from auxiliary translations and an acoustic anchor from speech; in principle, the mechanism can be applied to similar encoder--decoder multitask speech models. Through cross-modal adaptation, SAMA-ASR conditions decoder states on translation-derived semantic embeddings and a speech embedding, combining utterance-level meaning with speech-grounded evidence before token prediction. At evaluation time, these semantic anchors can be generated automatically by an upstream speech-to-text translator rather than supplied as oracle translations. Experiments on two 30-hour datasets covering the low-resource Sinitic varieties Taiwanese Hokkien and Hakka show that SAMA-ASR improves over acoustic, prior prompt-based, and semantic-only translation-guided baselines and remains effective in practical automatic semantic-anchor settings; translator-capacity analyses show that useful semantic anchors can be produced by a compact ST model. IEEE Journal of Translational Engineering in Health and Medicine, July 2026 Whenty Ariyanti, Ping-Yi Lin, Chia-Yu Hu, Shang-Feng Yang, Shin-Hung Tsai, Li-Ning Peng, Po-Hsun Huang, Hsin-Min Wang, and Yu Tsao Objective: Arteriovenous fistula (AVF) dysfunction is a major cause of morbidity in hemodialysis (HD) patients. Although conventional monitoring tools are clinically effective, they are often limited by cost, invasiveness, and operator dependence. This study proposes a cycle-aware, auscultation-based deep learning framework for objective and scalable assessment of AVF blood flow status. Methods and Procedures: We developed CALM, a physiologically informed learning framework that integrates Hilbert-based cycle segmentation with pretrained audio foundation model (AFM) representations. A clinical auscultation dataset (CHGH-AVF) from 188 HD patients at three standardized vascular access sites was collected and labeled using concurrent blood flow measurements. The proposed approach was evaluated on both three-class flow-severity classification (Low, Medium, and High) and binary flow-quality screening (Ideal vs. Unideal). Model variants incorporating domain-specific low-level descriptors and different feature fusion strategies were also investigated. Results: Across all auscultation sites, CALM consistently outperformed conventional handcrafted-feature baselines, deep learning baselines, and classical machine learning-based classifiers in patient-level flow-severity classification. Ablation studies demonstrated that physiologic cycle segmentation substantially improves discriminability, while integrating complementary low-level descriptors (LLDs) further enhanced performance across anatomically different recording locations. Although classification performance remained highest at the proximal sites, reduced performance at the venous site highlighted the influence of anatomical location and signal quality. Strong performance was also observed for binary flow-quality monitoring, particularly at the proximal auscultation sites. Conclusion: The proposed framework demonstrates the feasibility of AFM–based auscultation for non-invasive AVF flow assessment. By combining physiologic signal modeling with foundation model representations, CALM provides a robust and objective framework for automated assessment of vascular access function.Clinical Impact: This work introduces a multi-site clinical AVF auscultation dataset and establishes automated auscultation analysis as a low-cost and scalable adjunct to existing vascular access monitoring practices in HD care.Clinical and Translational Impact Statement: AFM-based auscultation has the potential to support objective screening of AVF dysfunction and may facilitate future integration into bedside or home monitoring systems, enabling more accessible and scalable vascular access monitoring. IEEE Journal of Biomedical and Health Informatics, July 2026 Whenty Ariyanti, Kuan-Yu Chen, Sabato Marco Siniscalchi, Hsin-Min Wang, and Yu Tsao Perceptual voice quality assessment plays a vital role in diagnosing and monitoring voice disorders.Traditional methods, such as the Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V) and the Grade, Roughness, Breathiness, Asthenia, and Strain (GRBAS) scales, rely on expert raters and are prone to inter-rater variability, emphasizing the need for objective solutions. This study introduces the Voice Quality Assessment Network (VOQANet), a deep learning framework that employs an attention mechanism and Speech Foundation Model (SFM) embeddings to extract high-level features. To further enhance performance, we propose VOQANet+, which integrates self-supervised SFM embeddings with low-level acoustic descriptors—namely jitter, shimmer, and harmonics-to-noise ratio (HNR). Unlike previous approaches that focus solely on vowel-based phonation (PVQD-A), our models are evaluated on both vowel-level and sentence-level speech (PVQD-S) to assess generalizability. Experimental results demonstrate that sentence-based inputs yield higher accuracy, particularly at the patient level. Overall, VOQANet consistently outperforms baseline models in terms of root mean squared error (RMSE) and Pearson correlation coefficient across CAPE-V and GRBAS dimensions, with VOQANet+ achieving even greater performance gains. Additionally, VOQANet+ maintains consistent performance under noisy conditions, suggesting enhanced robustness for real-world and telehealth applications. This work highlights the value of combining SFM embeddings with low-level features for accurate and robust pathological voice assessment. ACM International Conference on Multimedia (ACM MM), November 2026 Kuan-Lin Chu, Jun-Cheng Chen, Chun-Shien Lu As speech generation models become increasingly realistic and
widely accessible, concerns about the misuse, attribution, and governance
of synthetic speech continue to grow.Watermarking provides
a practical way to make synthesized speech traceable and verifiable.
Most existing speech watermarking methods embed watermark
information into signal-level representations, such as waveforms or
spectrograms. Under sufficiently strong distortions, the embedded
watermark may be weakened or destroyed, leading to degraded
detectability. In this paper, we propose SSTMark, a training-free
speech watermarking framework that operates at the semantic level
through text watermarking. Unlike conventional signal-level watermarking
methods, SSTMark encodes watermark information into
the semantic content conveyed by generated speech, and detects the
watermark from the recovered linguistic content. Experiments on
AudioMarkBench demonstrate that SSTMark exhibits the strongest
average robustness. Compared with the state-of-the-art baselines
at a fixed false positive rate of 1%, SSTMark improves the average
detection rate by 4.6% and 16.9% on signal-processing edits and
compression edits, respectively. European Conference on Computer Vision (ECCV), September 2026 Farchan Hakim Raswa, Chun-Shien Lu, and Jia-Ching Wang Recent studies demonstrate that Federated Learning (FL) is
vulnerable to gradient leakage attacks (GLAs). Revisiting key insights
in GLAs under FL reveals that (1) Defenses should focus on protecting
semantic and fine-grained details of data; (2) GLAs are effective mainly
in early rounds; and (3) To avoid semantic leakage, defenses shouldn’t
infer the true labels of private images for obfuscation. Building upon
these insights, we present a simple, time-adaptive defense strategy that
obfuscates the private gradient by employing the gradient from a synthesized
image. To this end, a client trains a diffusion model on its own
private dataset to generate synthesized images that fit the distribution
of private dataset but are distinct in fine-grained details. In addition, a
synthesized image is generated conditioned on a non-identical label from
the private image to resist semantic leakage. Defense analysis and empirical
evaluations demonstrate that our time-adaptive and label agnostic
method can better maintain the trade-off between privacy preservation
and model utility against GLAs. Our implementation is publicly available
at https://github.com/lalakitchen/Diff2Obs.
19th European Conference on Computer Vision (ECCV), September 2026 En-Ni Chuang, Hanjuan Huang, Hao-Jia Song, Hsing-Kuo Pao and Tyng-Luh Liu Generating functional neural-network weights from trained model collections is a central problem in weight-space learning. We introduce TiltDiff, a performance-tilted latent diffusion framework for neural-network generation. TiltDiff tokenizes network weights, encodes them into a compact latent space with a Transformer autoencoder, and uses a U-Net-based diffusion model to synthesize latent representations that decode into functional parameters. To favor stronger models, we weight the denoising loss by validation accuracy, biasing the learned distribution toward higher-performing regions of weight space. Experiments show that TiltDiff improves predictive performance, robustness to random parameter masking, and representational diversity over prior weight-generation methods. We further combine diffusion U-Net connectivity with attribution analysis to identify class-specific decision pathways. These pathways exhibit emergent correspondence across independently generated models despite differing raw parameters, and pathway-level masking verifies their importance for target-class prediction. Our results show that performance-tilted diffusion generates accurate, robust, diverse, and structurally interpretable neural-network weights. The 43rd International Conference on Machine Learning (ICML), July 2026 Chih-Jung Tsai, Hwann-Tzong Chen and Tyng-Luh Liu We present Creat3r, an iterative next-best-view (NBV) selection framework for efficient, high-quality 3D reconstruction. Starting from a small seed set of image--pose pairs, Creat3r repeatedly selects the most informative next camera pose. After each pose is chosen, the corresponding image is acquired and added to the multi-view set to update a 3DGS reconstruction. To guide selection, Creat3r constructs an intermediate point cloud and estimates reconstruction reliability via a novel \emph{3D confidence field}, which is projected to candidate poses through Gaussian projection to produce \emph{2D confidence} and \emph{exploration maps}. These maps balance exploitation of reliable regions and exploration of uncertain or unseen areas under computational constraints. Experiments with standard 3DGS show that Creat3r consistently outperforms baselines in novel view synthesis and surface reconstruction, achieving higher SSIM and F1 scores with fewer views. Briefings in Bioinformatics, June 2026 Tsai, B.S., Wong, J.Y., and Tsai, H.K.* Object detection has revolutionized multiple domains by enabling models to jointly classify and localize targets within data. Yet, its potential in genomic sequence analysis remains largely unexplored. Here, we introduce DNA-DETR, an adaptation of the DETR architecture for one-dimensional genomic object detection. Surprisingly, the direct application of object detection to DNA sequences yielded poor performance, even for elements with simple definitions such as Non-B DNA. We found that the widely used one-hot encoding failed to capture key structural features of several Non-B DNA types. To address this limitation, we systematically investigated how different sequence representations, including one-hot encoding, dot matrix, and their combination, affect detection accuracy and model generalization. Our experiments demonstrate that the choice of representation profoundly affects both localization and classification. Notably, the combined representation consistently outperformed single representations, particularly for complex sequence elements. Our findings suggest that there is no universal “one-representation-fits-all” solution in sequence feature learning. Despite the common perception that end-to-end learning diminishes the importance of representation, our results highlight that thoughtful selection of sequence representation remains critical for model design. IEEE Signal Processing Letters, June 2026 Chien-Chun Wang,Hung-Shin Lee,Hsin-Min Wang, and Berlin Chen Evaluating text-to-music (TTM) systems remains expensive because music impression (MI) and text alignment (TA) scores rely on human mean opinion scores (MOS). Most automatic MOS estimators are trained with point-wise regression or distributional classification. These objectives do not directly optimize rank-based metrics and provide weak geometric constraints for cross-modal coherence. To address these gaps, we propose DeRA-MOS, a decoupled optimization framework for TTM evaluation. For MI, we introduce a batch-aware listwise ranking loss that models relative order within each mini-batch and better aligns with evaluation based on Spearman's rank correlation coefficient (SRCC). For TA, we introduce a score-anchored modality alignment loss that maps human scores to target audio-text similarity and regularizes the latent space before fusion. By effectively mitigating the point-wise training mismatch and modality drift, experiments on MusicEval demonstrate that our decoupled framework yields substantial improvements in both MI and TA ranking metrics, establishing a robust paradigm for large-scale TTM evaluation. IEEE Transaction on Audio, Speech and Language Processing, February 2026 Chien-Chun Wang, Hung-Shin Lee, Hsin-Min Wang, and Berlin Chen Pre-trained models for automatic speech recognition (ASR) and speech enhancement (SE) have exhibited remarkable capabilities under matched noise and channel conditions. However, these models often suffer from severe performance degradation when confronted with domain shifts, particularly in the presence of unseen noise and channel distortions. In view of this, we in this paper present URSA-GAN, a unified and domain-aware generative framework specifically designed to mitigate mismatches in both noise and channel conditions. URSA-GAN leverages a dual-embedding architecture that consists of a noise encoder and a channel encoder, each pre-trained with limited in-domain data to capture domain-relevant representations. These embeddings condition a GAN-based speech generator, facilitating the synthesis of speech that is acoustically aligned with the target domain while preserving phonetic content. To enhance generalization further, we propose dynamic stochastic perturbation, a novel regularization technique that introduces controlled variability into the embeddings during generation, promoting robustness to unseen domains. Empirical results demonstrate that URSA-GAN effectively reduces character error rates in ASR and improves perceptual metrics in SE across diverse noisy and mismatched channel scenarios. Notably, evaluations on compound test conditions with both channel and noise degradations confirm the generalization ability of URSA-GAN, yielding relative improvements of 16.16% in ASR performance and 15.58% in SE metrics. IEEE Access, January 2026 Pei-Jun Liao, Hung-Yi Lee, and Hsin-Min Wang In automatic speech recognition (ASR), an emerging trend involves converting continuous speech features into sequences of discrete speech units (DSUs) via quantization. A key advantage of DSU representations is their compatibility with pretrained language models (PLMs), where DSUs are directly mapped to PLM token indices and the embedding layer is fine-tuned. However, this conventional strategy often relies heavily on large-scale training data to mitigate the inherent modality mismatch. In light of this, we explore a more effective way to exploit the PLM embedding dictionary. Drawing inspiration from Time-LLM, a recent time-series forecasting model, we propose a cross-attention reprogramming mechanism that incorporates codebook information from the DSU quantizer to better align the DSUs with the PLM embeddings. Compared to direct fine-tuning of PLM embeddings, our method consistently achieves improvements on the Discrete Audio and Speech Benchmark (DASB), reaching state-of-the-art performance across most DASB-style settings. We also evaluate our method on LibriSpeech-960, LibriLight-10, and Swedish, Czech, and Hungarian data from Common Voice, and observe similar trends. Notably, the proposed reprogramming method demonstrates significant gains over the fine-tuning baseline, particularly in cross-lingual and low-resource scenarios. This study proposes a new approach to using PLM embedding dictionaries in DSU-based ASR, and lays a foundation for combining speech representations with large language models in other discriminative tasks of speech processing such as speech emotion recognition and spoken question answering. Anchoring Speech with Semantics: A Multimodal Adapter Mechanism for Automatic Speech Recognition in Low-Resource Languages
Abstract
CALM: Cycle-Aware Latent Modeling for Arteriovenous Fistula Assessment using Audio Foundation Models and Low-Level Descriptors
Abstract
Towards Robust Assessment of Pathological Voices via Combined Low-Level Descriptors and Foundation Model Representations
Abstract
SSTMark: Robust Training-Free Semantic-Level Speech Watermarking
Abstract
Diffusion to Obfuscation: Time-Adaptive Synthesized Generation Against Gradient Leakage Attacks in Federated Learning
Abstract
TiltDiff: Tilted Weight-Space Diffusion for Neural Network Generation
Abstract
Creat3r: Confidence Reaggregation for Exploration-aware Active 3D Reconstruction
Abstract
DNA-DETR: sequence representation matters in object detection for functional genomic elements
Abstract
DeRA-MOS: Optimizing Text-to-Music Evaluation via Decoupled Listwise Ranking and Modality Alignment
Abstract
Universal Robust Speech Adaptation for Cross-Domain Speech Recognition and Enhancement
Abstract
Cross-Attention Reprogramming for ASR: Bridging Discrete Speech Units and Pretrained Language Models
Abstract
