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From the 1 of 8 linked papers with an AI index.

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8 papers

cs.CV2026

Learning Speaker Identity Beyond Language and Modality Constraints: Insights from the POLY-SIM 2026 Challenge

Marta Moscati, Muhammad Saad Saeed, Marina Zanoni +9

The paper describes the POLY-SIM 2026 challenge, which focuses on developing multimodal speaker identification systems that remain robust when audio or visual data are missing and…

cs.SD2026

Overview of ESDD2: Environment-Aware Speech and Sound Deepfake Detection Challenge

Xueping Zhang, Han Yin, Yang Xiao +4

The Environment-Aware Speech and Sound Deepfake Detection Challenge (ESDD2), held in conjunction with ICME 2026, evaluated systems for five component-level audio spoofing detection…

cs.CL2026

Adapting Self-Supervised Speech Representations for Cross-lingual Dysarthria Detection in Parkinson's Disease

Abner Hernandez, Eunjung Yeo, Kwanghee Choi +12

The limited availability of dysarthric speech data makes cross-lingual detection an important but challenging problem. A key difficulty is that speech representations often encode…

cs.SD2026

The First Environmental Sound Deepfake Detection Challenge: Benchmarking Robustness, Evaluation, and Insights

Han Yin, Yang Xiao, Rohan Kumar Das +2

Recent progress in audio generation has made it increasingly easy to create highly realistic environmental soundscapes, which can be misused to produce deceptive content, such as f…

cs.SD2026

A Survey of Large Audio Language Models: Generalization, Trustworthiness, and Outlook

Kaiwen Luo, Zhenhong Zhou, Leo Wang +34

Advances in Large Language Models (LLMs) have paved the way for Multimodal Large Language Models (MLLMs). Among these, Large Audio Language Models (LALMs) are essential for realizi…

cs.CV2026

POLY-SIM: Polyglot Speaker Identification with Missing Modality Grand Challenge 2026 Evaluation Plan

Marta Moscati, Muhammad Saad Saeed, Marina Zanoni +8

Multimodal speaker identification systems typically assume the availability of complete and homogeneous audio-visual modalities during both training and testing. However, in real-w…