collaborators

10 papers

cs.SD2026

Beyond Naturalness: Probing Automated Text-To-Speech Evaluators on Linguistically Grounded Dimensions

Oluwanifemi Bamgbose, Simon Rosen, Jash Shah +6

Automated Text-to-Speech (TTS) evaluation methods (Mean Opinion Score (MOS) predictors and Audio Large Language Models (Audio-LLM) judges) are expected to reflect human perception,…

cs.SD2026

From Sounds to Scenes: A Benchmark for Evaluating Context-Aware Auditory Scene Understanding in Large Audio Language Models

Pengfei Zhang, Hoang H Nguyen, Kazi Shaharair Sharif +6

Recent Large Audio Language Models (LALMs) have achieved remarkable progress in audio perceptual tasks across individual acoustic layers, including speech, sound, and music. Howeve…

cs.SD2026

AP-GRPO: Anchor-Gated Phonetic Alignment with Policy Optimization for Pathological Speech Reconstruction

Pengfei Zhang, Hoang H Nguyen, Yutong Song +6

Pathological speech from patients with neurodegenerative and neuromotor disorders is often acoustically distorted and linguistically fragmented, making pathological speech reconstr…

cs.SD2026

EVA-Bench: A New End-to-end Framework for Evaluating Voice Agents

Tara Bogavelli, Gabrielle Gauthier Melançon, Katrina Stankiewicz +10

Voice agents, artificial intelligence systems that conduct spoken conversations to complete tasks, are increasingly deployed across enterprise applications. However, no existing be…

cs.SD2026

AU-Harness: An Open-Source Toolkit for Holistic Evaluation of Audio LLMs

Hoang Nguyen, Sidharth Surapaneni, Akshay Kalkunte +9

Large Audio Language Models (LALMs) are rapidly advancing, but evaluating them remains challenging due to inefficient and non-standardized toolkits that limit fair comparison and s…

cs.CL2026

LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey

Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu +17

Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. However, fully autonomous LLM-based agents still face significant…