activity
20222026
most citedPromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming

1 citations · 1 across the 13 of their papers we have counts for

collaborators

13 papers

eess.AS2026

Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning

Xulin Fan, Jialu Li, Mohammad Nur Hossain Khan +4

Recent advances in model design and self-supervised audio representations have improved speech and audio understanding, yet infant-centered naturalistic recordings remain challengi…

cs.SD2026

Cloud-Boosted Low-Compute Multi-Channel Speech Enhancement

Xulin Fan, Juan Azcarreta, Ashutosh Pandey +5

Low-latency, low-compute speech enhancement is essential for wearable devices with real-time communication requirements, but strict computational constraints significantly limit on…

eess.AS2026

GigaSpeechBench: A Real-World Multilingual Speech-to-Text Benchmark

Yujie Tu, Yifan Yang, Tianrui Wang +35

While modern ASR systems achieve low error rates on high-resource benchmarks, such performance often overestimates real-world robustness. Existing evaluations address challenges in…

eess.AS2026

FSA-GRPO: Teaching Auditory LLMs to Use Few-Shot Demonstrations

Haolong Zheng, Siyin Wang, Xulin Fan +2

Few-shot prompting provides an effective way to adapt auditory large language models to low-resource tasks such as children's speech recognition. However, most auditory large langu…

eess.AS2026

In-Sync: Adaptation of Speech Aware Large Language Models for ASR with Word Level Timestamp Predictions

Xulin Fan, Vishal Sunder, Samuel Thomas +3

Recent advances in speech-aware language models have coupled strong acoustic encoders with large language models, enabling systems that move beyond transcription to produce richer…

cs.SD2026

AVMeme Exam: A Multimodal Multilingual Multicultural Benchmark for LLMs' Contextual and Cultural Knowledge and Thinking

Xilin Jiang, Qiaolin Wang, Junkai Wu +30

Internet audio-visual clips convey meaning through time-varying sound and motion, which extend beyond what text alone can represent. To examine whether AI models can understand suc…

eess.AS2025

Automated Analysis of Naturalistic Recordings in Early Childhood: Applications, Challenges, and Opportunities

Jialu Li, Marvin Lavechin, Xulin Fan +4

Naturalistic recordings capture audio in real-world environments where participants behave naturally without interference from researchers or experimental protocols. Naturalistic l…

cs.CL2025

ProsodyLM: Uncovering the Emerging Prosody Processing Capabilities in Speech Language Models

Kaizhi Qian, Xulin Fan, Junrui Ni +4

Speech language models refer to language models with speech processing and understanding capabilities. One key desirable capability for speech language models is the ability to cap…

eess.AS2025

ArrayDPS: Unsupervised Blind Speech Separation with a Diffusion Prior

Zhongweiyang Xu, Xulin Fan, Zhong-Qiu Wang +2

Blind Speech Separation (BSS) aims to separate multiple speech sources from audio mixtures recorded by a microphone array. The problem is challenging because it is a blind inverse…

cs.LG2025

SyncDiff: Diffusion-based Talking Head Synthesis with Bottlenecked Temporal Visual Prior for Improved Synchronization

Xulin Fan, Heting Gao, Ziyi Chen +3

Talking head synthesis, also known as speech-to-lip synthesis, reconstructs the facial motions that align with the given audio tracks. The synthesized videos are evaluated on mainl…

cs.CL2024

Just ASR + LLM? A Study on Speech Large Language Models' Ability to Identify and Understand Speaker in Spoken Dialogue

Junkai Wu, Xulin Fan, Bo-Ru Lu +4

In recent years, we have observed a rapid advancement in speech language models (SpeechLLMs), catching up with humans' listening and reasoning abilities. SpeechLLMs have demonstrat…

cs.CL2023★ 1 cited

PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming

Chufan Gao, Xulin Fan, Jimeng Sun +1

Relation extraction aims to classify the relationships between two entities into pre-defined categories. While previous research has mainly focused on sentence-level relation extra…

cs.MM2022

Dual-Path Cross-Modal Attention for better Audio-Visual Speech Extraction

Zhongweiyang Xu, Xulin Fan, Mark Hasegawa-Johnson

Audio-visual target speech extraction, which aims to extract a certain speaker's speech from the noisy mixture by looking at lip movements, has made significant progress combining…