7 citations · 7 across the 14 of their papers we have counts for
14 papers
Listen, Critique, and Refine: RL-Based Self-Refinement for Instruction-Following Speech Synthesis
Chee-En Yu, Yi-Cheng Lin, Sung-Feng Huang +4
Large Audio Language Models (LALMs) can follow diverse instructions to synthesize speech in specified styles. However, complex instructions that require simultaneous control over p…
Bridging Static and Agentic RAG for Taiwanese Historical Question Answering
Kai-Hsin Chen, Wei-Yu Chen, Xuanjun Chen +1
Agentic retrieval-augmented generation (RAG) enables language models to adapt retrieval based on previously retrieved evidence, but it remains unclear whether such adaptive orchest…
Only Ask What You Don't Know: Grounded Delta Planning for Efficient Multi-step RAG
Wei-Chieh Chou, Xuanjun Chen, Jian-Ren Lin +3
Multi-hop question answering remains challenging for Retrieval-Augmented Generation (RAG) because existing approaches either propagate errors across iterative retrieval rounds or o…
CodaRAG: Connecting the Dots with Associativity Inspired by Complementary Learning
Cheng-Yen Li, Xuanjun Chen, Claire Lin +4
Large Language Models (LLMs) struggle with knowledge-intensive tasks due to hallucinations and fragmented reasoning over dispersed information. While Retrieval-Augmented Generation…
Joint Fullband-Subband Modeling for High-Resolution SingFake Detection
Xuanjun Chen, Chia-Yu Hu, Sung-Feng Huang +3
Rapid advances in singing voice synthesis have increased unauthorized imitation risks, creating an urgent need for better Singing Voice Deepfake (SingFake) Detection, also known as…
A Preliminary Study of RAG for Taiwanese Historical Archives
Claire Lin, Bo-Han Feng, Xuanjun Chen +3
Retrieval-Augmented Generation (RAG) has emerged as a promising approach for knowledge-intensive tasks. However, few studies have examined RAG for Taiwanese Historical Archives. In…
Localizing Audio-Visual Deepfakes via Hierarchical Boundary Modeling
Xuanjun Chen, Shih-Peng Cheng, Jiawei Du +6
Audio-visual temporal deepfake localization under the content-driven partial manipulation remains a highly challenging task. In this scenario, the deepfake regions are usually only…
Towards Generalized Source Tracing for Codec-Based Deepfake Speech
Xuanjun Chen, I-Ming Lin, Lin Zhang +3
Recent attempts at source tracing for codec-based deepfake speech (CodecFake), generated by neural audio codec-based speech generation (CoSG) models, have exhibited suboptimal perf…
Codec-Based Deepfake Source Tracing via Neural Audio Codec Taxonomy
Xuanjun Chen, I-Ming Lin, Lin Zhang +4
Recent advances in neural audio codec-based speech generation (CoSG) models have produced remarkably realistic audio deepfakes. We refer to deepfake speech generated by CoSG system…
Singing Voice Graph Modeling for SingFake Detection
Xuanjun Chen, Haibin Wu, Jyh-Shing Roger Jang +1
Detecting singing voice deepfakes, or SingFake, involves determining the authenticity and copyright of a singing voice. Existing models for speech deepfake detection have struggled…
Neural Codec-based Adversarial Sample Detection for Speaker Verification
Xuanjun Chen, Jiawei Du, Haibin Wu +2
Automatic Speaker Verification (ASV), increasingly used in security-critical applications, faces vulnerabilities from rising adversarial attacks, with few effective defenses availa…
Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detection
Xuanjun Chen, Haibin Wu, Helen Meng +2
Audio-visual active speaker detection (AVASD) is well-developed, and now is an indispensable front-end for several multi-modal applications. However, to the best of our knowledge,…
Multimodal Transformer Distillation for Audio-Visual Synchronization
Xuanjun Chen, Haibin Wu, Chung-Che Wang +2
Audio-visual synchronization aims to determine whether the mouth movements and speech in the video are synchronized. VocaLiST reaches state-of-the-art performance by incorporating…
Adversarial Speaker Distillation for Countermeasure Model on Automatic Speaker Verification
Yen-Lun Liao, Xuanjun Chen, Chung-Che Wang +1
The countermeasure (CM) model is developed to protect ASV systems from spoof attacks and prevent resulting personal information leakage in Automatic Speaker Verification (ASV) syst…