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SpeechParaling-Bench: A Comprehensive Benchmark for Paralinguistic-Aware Speech Generation
Ruohan Liu, Shukang Yin, Tao Wang +6
Paralinguistic cues are essential for natural human-computer interaction, yet their evaluation in Large Audio-Language Models (LALMs) remains limited by coarse feature coverage and…
WESR: Scaling and Evaluating Word-level Event-Speech Recognition
Chenchen Yang, Kexin Huang, Liwei Fan +8
Speech conveys not only linguistic information but also rich non-verbal vocal events such as laughing and crying. While semantic transcription is well-studied, the precise localiza…
UnifiedVisual: A Framework for Constructing Unified Vision-Language Datasets
Pengyu Wang, Shaojun Zhou, Chenkun Tan +7
Unified vision large language models (VLLMs) have recently achieved impressive advancements in both multimodal understanding and generation, powering applications such as visual qu…
Decoupled Proxy Alignment: Mitigating Language Prior Conflict for Multimodal Alignment in MLLM
Chenkun Tan, Pengyu Wang, Shaojun Zhou +6
Multimodal large language models (MLLMs) have gained significant attention due to their impressive ability to integrate vision and language modalities. Recent advancements in MLLMs…
InstructTTSEval: Benchmarking Complex Natural-Language Instruction Following in Text-to-Speech Systems
Kexin Huang, Qian Tu, Liwei Fan +6
In modern speech synthesis, paralinguistic information--such as a speaker's vocal timbre, emotional state, and dynamic prosody--plays a critical role in conveying nuance beyond mer…
MetaAlign: Align Large Language Models with Diverse Preferences during Inference Time
Mozhi Zhang, Pengyu Wang, Chenkun Tan +4
Large Language Models (LLMs) acquire extensive knowledge and remarkable abilities from extensive text corpora, making them powerful tools for various applications. To make LLMs mor…