arXiv Computer Vision

Laryngeal Structure Segmentation in High-Speed Videoendoscopy Using Deep Learning

The study presents deep learning‑based segmentation of laryngeal structures in high‑speed videoendoscopy (HSV) data, including connected speech. Using U‑Net models, the authors trained networks to detect aryepiglottic folds, arytenoid cartilages, vocal folds, epiglottis, and glottal area from HSV recordings of both sustained vowel phonation and connected speech in normal and disordered voices. After applying noise removal and histogram equalization, the networks achieved overall accuracies above 95%, demonstrating reliable automated analysis of laryngeal dynamics.

arXiv Machine Learning

Sep 29

BreathGRU: A Novel Semi-Supervised Bidirectional Gated Recurrent Unit Framework for Speech and Breath Segmentation for Respiratory Audio

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

computer-vision

arXiv Machine Learning

Jun 29

Advancing Speaker-Based Vocal Effort Classification with WavLM and Data Augmentation in Naturalistic Non-Calibrated Speech Recordings

arXiv:2606. 27543v1 Announce Type: cross Abstract: The variations in vocal effort range (e.

By Zahra Omidi, John H. L. Hansen

multimodal benchmarks

arXiv Machine Learning

2d ago

Self-Supervised Speech Representations for Cross-Speaker Dysarthria Detection During Awake Craniotomy

arXiv:2610.11825v1 Announce Type: new Abstract: Detecting intra-operative speech impairment during awake craniotomy is essential for preserving language function. However, automated detection remains...

By Kanthila Chinmayi (IRDL, LaTIM), Abdallah Nassib (LARIS), Misy Harrison (LaTIM), Panheleux Celine (LaTIM, CHU - BREST), Saliou Vanessa (CHU - BREST), Seizeur Romuald (LaTIM), Dardenne Guillaume (LaTIM)

rag

arXiv Computer Vision

Sep 10

Arti-JEPA: Adapting Video World Model to Real-Time MRI of the Vocal Tract for Speech-Production Analysis

arXiv:2609.09757v1 Announce Type: cross Abstract: Real-time MRI (rtMRI) captures the dynamics of the entire vocal tract during speech, but labeled data are scarce and the modality - single-slice, gra...

By Hong Nguyen, Sean Foley, Christina Hagedorn, Yijing Lu, Sudarsana Reddy Kadiri, Dani Byrd, Shrikanth Narayanan

rag fine-tuning

arXiv Computation and Language

Sep 30

Reconstructing the Vocal Tract with Differentiable Acoustic Simulation

arXiv:2609.36737v1 Announce Type: cross Abstract: The vocal tract is the region of the human body responsible for filtering one's voice to create speech. In this paper, we present a differentiable an...

By Eric Ming Chen, Jin Woo Lee, Vincent Sitzmann

arXiv AI

Jun 19

Improving End-to-End Speech Recognition for Dysarthric Speech through In-Domain Data Augmentation

arXiv:2606. 19797v1 Announce Type: cross Abstract: Dysarthric speech recognition is crucial for facilitating effective communication among individuals with dysarthria.

By Paban Sapkota, Hemant Kumar Kathania, Sudarsana Reddy Kadiri, Shrikanth Narayanan

fine-tuning multimodal

arXiv AI

Jun 10

Optimizing 2D Input Representations and Sub-phase Fusion Strategies for Differential Diagnosis of Asthma and COPD Using CNN- and GRU-Based Networks

arXiv:2606. 10972v1 Announce Type: cross Abstract: This study aims to explore the performance of the VAR model in comparison with mel-frequency cepstral coefficient (MFCC) matrices and log-mel spectrograms using deep learning.

By Ipek Sen, Ozgur Ozdemir, Elena Battini Sonmez

arXiv AI

Jun 29

EAGT: Echocardiography Augmentation for Generalisability and Transferability

arXiv:2605. 16427v2 Announce Type: replace-cross Abstract: Deep learning models for echocardiography segmentation often struggle to generalise across institutions, scanners, and patient populations, where collecting large, consistently annotated datasets is infeasible.

By Soroush Elyasi, Sara Adibzadeh, Nasim Dadashi Serej, Massoud Zolgharni

computer-vision

arXiv AI

Aug 18

Comprehensive Benchmarking of Deep Learning Architectures for Lung Cancer Histopathology

arXiv:2608. 15915v1 Announce Type: cross Abstract: Lung cancer remains the leading cause of cancer-related mortality worldwide, while histopathological diagnosis is often affected by inter-observer variability and the substantial workload associated with manual slide examination.

By Hadi Hasan, Safaa Salman, Lama Sleem, Ralph Mouawad, Ali Chehab

llms computer-vision benchmarks

arXiv AI

Aug 25

Multi-Task Learning for Non-Canonical Phoneme Recognition via Articulatory Feature Decomposition

The paper proposes a linguistically structured multi‑task learning framework for recognizing non‑canonical phonemes by decomposing phoneme prediction into articulatory feature dimensions such as manner, place, and voicing. A hierarchical architecture with task‑specific heads and a cross‑attention fusion module is combined with semi‑supervised Momentum Pseudo‑Labeling and a cascaded training strategy that gradually introduces articulatory tasks. Experiments on the L2‑ARCTIC dataset demonstrate significant improvements over baseline models and produce interpretable error patterns aligned with phonological feature structure.

By Sophia Riaz, Haoze Zheng, Amos Roche, Miyu Zhang, Anamika Ragu, Salvatore Penachio, Kaustav Mukherjee, Aneesh Jonelagadda

arXiv Computation and Language

Oct 1

VOSSA: Voiceprint Optimization for Streaming Speech Architectures

arXiv:2609.38887v1 Announce Type: cross Abstract: Real-time voice conversion (VC) systems commonly rely on pretrained speaker embeddings from automatic speaker verification (ASV) models. While effect...

By Mu-Ruei Tseng, Waris Quamer, Ghady Nasrallah, Ricardo Gutierrez-Osuna

rag

arXiv Computer Vision

Sep 18

KoUniTalk: A Lightweight Articulation-Centered Korean-English 3D Talking Face Benchmark

KoUniTalk is a lightweight, articulation‑centered benchmark that unifies Korean and English 3D talking‑face datasets onto a single mesh topology. By retargeting VOCASET and Korean speech‑based 3D data to a shared 1,176‑vertex template, it reduces output dimensionality from tens of thousands to 3,528 dimensions, focusing on the mouth and adjacent lower‑face regions. The benchmark includes 22 speakers, 4,978 sequences, and 642,781 frames, enabling controlled speech‑driven facial articulation training and cross‑dataset evaluation in a compact, identity‑neutral space.

By Hyunjung Chung, Unsang Park

benchmarks

Related stories