5 citations · 8 across the 12 of their papers we have counts for
6 papers · 1 filter
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…