6 citations · 7 across the 9 of their papers we have counts for
18 papers
MoVE: Translating Laughter and Tears via Mixture of Vocalization Experts in Speech-to-Speech Translation
Szu-Chi Chen, I-Ning Tsai, Yi-Cheng Lin +2
Recent Speech-to-Speech Translation (S2ST) systems achieve strong semantic accuracy yet consistently strip away non-verbal vocalizations (NVs), such as laughter and crying that con…
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…
VIBE: Voice-Induced open-ended Bias Evaluation for Large Audio-Language Models via Real-World Speech
Yi-Cheng Lin, Yusuke Hirota, Sung-Feng Huang +1
Large Audio-Language Models (LALMs) are increasingly integrated into daily applications, yet their generative biases remain underexplored. Existing speech fairness benchmarks rely…
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…
Maximizing Data Efficiency for Cross-Lingual TTS Adaptation by Self-Supervised Representation Mixing and Embedding Initialization
Wei-Ping Huang, Sung-Feng Huang, Hung-yi Lee
This paper presents an effective transfer learning framework for language adaptation in text-to-speech systems, with a focus on achieving language adaptation using minimal labeled…
Personalized Lightweight Text-to-Speech: Voice Cloning with Adaptive Structured Pruning
Sung-Feng Huang, Chia-ping Chen, Zhi-Sheng Chen +2
Personalized TTS is an exciting and highly desired application that allows users to train their TTS voice using only a few recordings. However, TTS training typically requires many…