9 papers
MemeLogos: Knowledge-Driven Retrieval-Augmented Reasoning for Harmful Meme Detection
Hexiang Gu, Saihui Hou, Changlan Liu +4
With the rapid proliferation of social media, harmful memes have become a growing threat to online ecosystems. Their harmfulness is often implicitly conveyed through complex intera…
DualSpecSE: A Dual-Path Speech Enhancement Network Integrating Mel and Complex Spectrograms
Xingchen Li, Ziqian Wang, Zikai Liu +4
In this paper, we propose DualSpecSE, a speech enhancement framework that jointly models Mel-spectrogram and complex spectrogram in a dual-path architecture for improved ASR perfor…
The SLT 2026 SmartGlasses Challenge: Benchmarking Egocentric Multi-Talker Speech Recognition and Understanding with Audio-Language Models
Dehui Gao, Zhixian Zhao, Zhennan Lin +14
Recent advances in large language models (LLMs) and multimodal LLMs (MLLMs) have created new opportunities for wearable speech interfaces, with smart glasses providing an egocentri…
G-MaP-SE: Guided Speech Enhancement via GMM-Based Prior Matching
Yike Zhu, Ziqian Wang, Zikai Liu +5
Using speaker embeddings as conditioning can strengthen speech enhancement, but most methods either require clean enrollment audio or rely on embeddings extracted from noisy speech…
EvoTSE: Evolving Enrollment for Target Speaker Extraction
Zikai Liu, Ziqian Wang, Xingchen Li +4
Target Speaker Extraction (TSE) aims to isolate a specific speaker's voice from a mixture, guided by a pre-recorded enrollment. While TSE bypasses the global permutation ambiguity…
MeanFlowSE: One-Step Generative Speech Enhancement via MeanFlow
Yike Zhu, Boyi Kang, Ziqian Wang +6
Speech enhancement (SE) recovers clean speech from noisy signals and is vital for applications such as telecommunications and automatic speech recognition (ASR). While generative a…
UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling
Ziqian Wang, Zikai Liu, Yike Zhu +6
Generative modeling has recently achieved remarkable success across image, video, and audio domains, demonstrating powerful capabilities for unified representation learning. Yet sp…
FlowSE: Efficient and High-Quality Speech Enhancement via Flow Matching
Ziqian Wang, Zikai Liu, Xinfa Zhu +6
Generative models have excelled in audio tasks using approaches such as language models, diffusion, and flow matching. However, existing generative approaches for speech enhancemen…
LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement
Boyi Kang, Xinfa Zhu, Zihan Zhang +10
Recent advancements in language models (LMs) have demonstrated strong capabilities in semantic understanding and contextual modeling, which have flourished in generative speech enh…