5 citations · 8 across the 12 of their papers we have counts for
14 papers
Can LLM Agents Automate Reinforcement Learning for Text-to-Speech?
Xuanjun Chen, Zixiong Su, Hao Shi +4
Although reinforcement learning (RL) post-training repairs the localized segmental errors of zero-shot text-to-speech (TTS), arriving at a working recipe still relies on tedious ma…
Mitigating Proxy-to-Wild Domain Gap in Deepfake Speech
Xuanjun Chen, Yun-Shing Wu, Wei-Chung Lu +4
Recent neural audio codec-based speech generation (CodecFake) produces highly realistic audio, posing a challenge to existing deepfake countermeasure models. While using codec resy…
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…
How Does Instrumental Music Help SingFake Detection?
Xuanjun Chen, Chia-Yu Hu, I-Ming Lin +8
Although many models exist to detect singing voice deepfakes (SingFake), how these models operate, particularly with instrumental accompaniment, is unclear. We investigate how inst…
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…
CodecFake+: Codec-Based Resynthesized Data as a Proxy for Detecting CodecFake Speech
Xuanjun Chen, Jiawei Du, Haibin Wu +8
With the rapid advancement of neural audio codecs, codec-based speech generation (CoSG) systems have become highly powerful. Unfortunately, CoSG also enables the creation of highly…
DFADD: The Diffusion and Flow-Matching Based Audio Deepfake Dataset
Jiawei Du, I-Ming Lin, I-Hsiang Chiu +6
Mainstream zero-shot TTS production systems like Voicebox and Seed-TTS achieve human parity speech by leveraging Flow-matching and Diffusion models, respectively. Unfortunately, hu…
Efficient Training of Self-Supervised Speech Foundation Models on a Compute Budget
Andy T. Liu, Yi-Cheng Lin, Haibin Wu +2
Despite their impressive success, training foundation models remains computationally costly. This paper investigates how to efficiently train speech foundation models with self-sup…
EMO-Codec: An In-Depth Look at Emotion Preservation capacity of Legacy and Neural Codec Models With Subjective and Objective Evaluations
Wenze Ren, Yi-Cheng Lin, Huang-Cheng Chou +5
The neural codec model reduces speech data transmission delay and serves as the foundational tokenizer for speech language models (speech LMs). Preserving emotional information in…
CodecFake: Enhancing Anti-Spoofing Models Against Deepfake Audios from Codec-Based Speech Synthesis Systems
Haibin Wu, Yuan Tseng, Hung-yi Lee
Current state-of-the-art (SOTA) codec-based audio synthesis systems can mimic anyone's voice with just a 3-second sample from that specific unseen speaker. Unfortunately, malicious…
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…