34 citations · 81 across the 18 of their papers we have counts for
18 papers
RADAR Challenge 2026: Robust Audio Deepfake Recognition under Media Transformations
Hieu-Thi Luong, Xuechen Liu, Ivan Kukanov +2
RADAR Challenge 2026 is an APSIPA Grand Challenge on Robust Audio Deepfake Recognition under Media Transformations, designed to simulate realistic media conditions in real-world au…
WeDefense: A Toolkit to Defend Against Fake Audio
Lin Zhang, Johan Rohdin, Xin Wang +8
The advances in generative AI have enabled the creation of synthetic audio which is perceptually indistinguishable from real, genuine audio. Although this stellar progress enables…
Robust Localization of Partially Fake Speech: Metrics and Out-of-Domain Evaluation
Hieu-Thi Luong, Inbal Rimon, Haim Permuter +2
Partial audio deepfake localization poses unique challenges and remain underexplored compared to full-utterance spoofing detection. While recent methods report strong in-domain per…
NTU-NPU System for Voice Privacy 2024 Challenge
Nikita Kuzmin, Hieu-Thi Luong, Jixun Yao +3
In this work, we describe our submissions for the Voice Privacy Challenge 2024. Rather than proposing a novel speech anonymization system, we enhance the provided baselines to meet…
Room Impulse Responses help attackers to evade Deep Fake Detection
Hieu-Thi Luong, Duc-Tuan Truong, Kong Aik Lee +1
The ASVspoof 2021 benchmark, a widely-used evaluation framework for anti-spoofing, consists of two subsets: Logical Access (LA) and Deepfake (DF), featuring samples with varied cod…
LlamaPartialSpoof: An LLM-Driven Fake Speech Dataset Simulating Disinformation Generation
Hieu-Thi Luong, Haoyang Li, Lin Zhang +2
Previous fake speech datasets were constructed from a defender's perspective to develop countermeasure (CM) systems without considering diverse motivations of attackers. To better…
Temporal-Channel Modeling in Multi-head Self-Attention for Synthetic Speech Detection
Duc-Tuan Truong, Ruijie Tao, Tuan Nguyen +3
Recent synthetic speech detectors leveraging the Transformer model have superior performance compared to the convolutional neural network counterparts. This improvement could be du…
LaughNet: synthesizing laughter utterances from waveform silhouettes and a single laughter example
Hieu-Thi Luong, Junichi Yamagishi
Emotional and controllable speech synthesis is a topic that has received much attention. However, most studies focused on improving the expressiveness and controllability in the co…
Preliminary study on using vector quantization latent spaces for TTS/VC systems with consistent performance
Hieu-Thi Luong, Junichi Yamagishi
Generally speaking, the main objective when training a neural speech synthesis system is to synthesize natural and expressive speech from the output layer of the neural network wit…
Latent linguistic embedding for cross-lingual text-to-speech and voice conversion
Hieu-Thi Luong, Junichi Yamagishi
As the recently proposed voice cloning system, NAUTILUS, is capable of cloning unseen voices using untranscribed speech, we investigate the feasibility of using it to develop a uni…
NAUTILUS: a Versatile Voice Cloning System
Hieu-Thi Luong, Junichi Yamagishi
We introduce a novel speech synthesis system, called NAUTILUS, that can generate speech with a target voice either from a text input or a reference utterance of an arbitrary source…
Bootstrapping non-parallel voice conversion from speaker-adaptive text-to-speech
Hieu-Thi Luong, Junichi Yamagishi
Voice conversion (VC) and text-to-speech (TTS) are two tasks that share a similar objective, generating speech with a target voice. However, they are usually developed independentl…
A Unified Speaker Adaptation Method for Speech Synthesis using Transcribed and Untranscribed Speech with Backpropagation
Hieu-Thi Luong, Junichi Yamagishi
By representing speaker characteristic as a single fixed-length vector extracted solely from speech, we can train a neural multi-speaker speech synthesis model by conditioning the…
Training Multi-Speaker Neural Text-to-Speech Systems using Speaker-Imbalanced Speech Corpora
Hieu-Thi Luong, Xin Wang, Junichi Yamagishi +1
When the available data of a target speaker is insufficient to train a high quality speaker-dependent neural text-to-speech (TTS) system, we can combine data from multiple speakers…
Multimodal speech synthesis architecture for unsupervised speaker adaptation
Hieu-Thi Luong, Junichi Yamagishi
This paper proposes a new architecture for speaker adaptation of multi-speaker neural-network speech synthesis systems, in which an unseen speaker's voice can be built using a rela…
Investigating accuracy of pitch-accent annotations in neural network-based speech synthesis and denoising effects
Hieu-Thi Luong, Xin Wang, Junichi Yamagishi +1
We investigated the impact of noisy linguistic features on the performance of a Japanese speech synthesis system based on neural network that uses WaveNet vocoder. We compared an i…
Wasserstein GAN and Waveform Loss-based Acoustic Model Training for Multi-speaker Text-to-Speech Synthesis Systems Using a WaveNet Vocoder
Yi Zhao, Shinji Takaki, Hieu-Thi Luong +3
Recent neural networks such as WaveNet and sampleRNN that learn directly from speech waveform samples have achieved very high-quality synthetic speech in terms of both naturalness…
Scaling and bias codes for modeling speaker-adaptive DNN-based speech synthesis systems
Hieu-Thi Luong, Junichi Yamagishi
Most neural-network based speaker-adaptive acoustic models for speech synthesis can be categorized into either layer-based or input-code approaches. Although both approaches have t…