BenSparX introduces the first Bengali conversational speech dataset for Parkinson’s disease detection and pairs it with a robust, explainable machine learning framework. The framework uses diverse acoustic features, systematic feature selection, and advanced classifiers, achieving 95.67% accuracy, 95.62% F1, and 0.990 AUC. SHAP analysis is employed to explain feature contributions, and the model outperforms state‑of‑the‑art methods on other language datasets.
By Riad Hossain, Muhammad Ashad Kabir, Arat Ibne Golam Mowla, Animesh Chandra Roy, Ranjit Kumar Ghosh
The paper introduces BTS-CAFE, a federated domain generalization framework for respiratory sound classification that addresses stethoscope-induced shortcuts. It combines causality-inspired device-style interventions, counterfactual metadata augmentation, and gradient alignment to reduce style–content entanglement and promote device-invariant decision boundaries. Experiments on ICBHI and SPRSound datasets show a 3.69‑point improvement in out-of-distribution performance over the baseline and outperform conventional data augmentation and federated learning methods.
By Heejoon Koo, Yoon Tae Kim, Miika Toikkanen, June-Woo Kim
arXiv:2610.18934v1 Announce Type: cross
Abstract: Conventional deepfake speech detectors primarily target synthesis artifacts and often degrade under unseen attacks or recording conditions. Human spe...
By Vojt\v{e}ch Stan\v{e}k, Michal Fri\v{c}, Kamil Malinka, Anton Firc
arXiv:2607. 00986v1 Announce Type: new Abstract: Automatically detecting stress in speech provides an unobtrusive way to gain insights relevant to behavioral research or clinical assessment.
By Hanna Drimalla, Wieland R. Cremer, Christine Kraus, Oliver T. Wolf
BreathGRU is a semi‑supervised Bidirectional Gated Recurrent Unit framework designed to segment speech and breath events in respiratory audio. It combines acoustic feature extraction, bidirectional recurrent modeling, pseudo‑label refinement, and duration‑constrained Segmental Viterbi decoding to produce accurate speech‑breath segmentation. In evaluations against existing methods, BreathGRU achieved the highest breath event recall, lowest onset‑localisation error, and highest Mean Match Intersection over Union, outperforming large pretrained VAD models such as Silero.
By Sania Fatima Sayed, John W. Holloway, Reyer Zwiggelaar, Faisal I. Rezwan
The paper introduces an interpretable framework for singing emotion recognition that uses a compact set of singing-specific descriptors covering prosody, spectral features, phonetics, and vocal technique. By applying SHAP and GRAD-CAM, the authors link model predictions to measurable acoustic phenomena, showing that vocal technique descriptors reduce cross-singer variability and provide interpretable insights. Out‑of‑domain tests on VocalSet reveal that vibrato and pharyngeal techniques are key emotional cues, with vibrato’s importance depending on articulation rate.
By Fotini Kiriakopoulou, Vassilis Lyberatos, Spyridon Kantarelis, Athanasios Voulodimos, Giorgos Stamou