84 citations · 233 across the 24 of their papers we have counts for
24 papers
SimpleSpeech 2: Towards Simple and Efficient Text-to-Speech with Flow-based Scalar Latent Transformer Diffusion Models
Dongchao Yang, Rongjie Huang, Yuanyuan Wang +5
Scaling Text-to-speech (TTS) to large-scale datasets has been demonstrated as an effective method for improving the diversity and naturalness of synthesized speech. At the high lev…
UniAudio: An Audio Foundation Model Toward Universal Audio Generation
Dongchao Yang, Jinchuan Tian, Xu Tan +9
Large Language models (LLM) have demonstrated the capability to handle a variety of generative tasks. This paper presents the UniAudio system, which, unlike prior task-specific app…
SnakeGAN: A Universal Vocoder Leveraging DDSP Prior Knowledge and Periodic Inductive Bias
Sipan Li, Songxiang Liu, Luwen Zhang +5
Generative adversarial network (GAN)-based neural vocoders have been widely used in audio synthesis tasks due to their high generation quality, efficient inference, and small compu…
The Singing Voice Conversion Challenge 2023
Wen-Chin Huang, Lester Phillip Violeta, Songxiang Liu +2
We present the latest iteration of the voice conversion challenge (VCC) series, a bi-annual scientific event aiming to compare and understand different voice conversion (VC) system…
HiFi-Codec: Group-residual Vector quantization for High Fidelity Audio Codec
Dongchao Yang, Songxiang Liu, Rongjie Huang +3
Audio codec models are widely used in audio communication as a crucial technique for compressing audio into discrete representations. Nowadays, audio codec models are increasingly…
Diverse and Expressive Speech Prosody Prediction with Denoising Diffusion Probabilistic Model
Xiang Li, Songxiang Liu, Max W. Y. Lam +3
Expressive human speech generally abounds with rich and flexible speech prosody variations. The speech prosody predictors in existing expressive speech synthesis methods mostly pro…
InstructTTS: Modelling Expressive TTS in Discrete Latent Space with Natural Language Style Prompt
Dongchao Yang, Songxiang Liu, Rongjie Huang +2
Expressive text-to-speech (TTS) aims to synthesize different speaking style speech according to human's demands. Nowadays, there are two common ways to control speaking styles: (1)…
NoreSpeech: Knowledge Distillation based Conditional Diffusion Model for Noise-robust Expressive TTS
Dongchao Yang, Songxiang Liu, Jianwei Yu +3
Expressive text-to-speech (TTS) can synthesize a new speaking style by imiating prosody and timbre from a reference audio, which faces the following challenges: (1) The highly dyna…
Speaker Identity Preservation in Dysarthric Speech Reconstruction by Adversarial Speaker Adaptation
Disong Wang, Songxiang Liu, Xixin Wu +4
Dysarthric speech reconstruction (DSR), which aims to improve the quality of dysarthric speech, remains a challenge, not only because we need to restore the speech to be normal, bu…
DiffGAN-TTS: High-Fidelity and Efficient Text-to-Speech with Denoising Diffusion GANs
Songxiang Liu, Dan Su, Dong Yu
Denoising diffusion probabilistic models (DDPMs) are expressive generative models that have been used to solve a variety of speech synthesis problems. However, because of their hig…
Meta-Voice: Fast few-shot style transfer for expressive voice cloning using meta learning
Songxiang Liu, Dan Su, Dong Yu
The task of few-shot style transfer for voice cloning in text-to-speech (TTS) synthesis aims at transferring speaking styles of an arbitrary source speaker to a target speaker's vo…
Referee: Towards reference-free cross-speaker style transfer with low-quality data for expressive speech synthesis
Songxiang Liu, Shan Yang, Dan Su +1
Cross-speaker style transfer (CSST) in text-to-speech (TTS) synthesis aims at transferring a speaking style to the synthesised speech in a target speaker's voice. Most previous CSS…
ASR-GLUE: A New Multi-task Benchmark for ASR-Robust Natural Language Understanding
Lingyun Feng, Jianwei Yu, Deng Cai +3
Language understanding in speech-based systems have attracted much attention in recent years with the growing demand for voice interface applications. However, the robustness of na…
DiffSVC: A Diffusion Probabilistic Model for Singing Voice Conversion
Songxiang Liu, Yuewen Cao, Dan Su +1
Singing voice conversion (SVC) is one promising technique which can enrich the way of human-computer interaction by endowing a computer the ability to produce high-fidelity and exp…
VARA-TTS: Non-Autoregressive Text-to-Speech Synthesis based on Very Deep VAE with Residual Attention
Peng Liu, Yuewen Cao, Songxiang Liu +4
This paper proposes VARA-TTS, a non-autoregressive (non-AR) text-to-speech (TTS) model using a very deep Variational Autoencoder (VDVAE) with Residual Attention mechanism, which re…
FastSVC: Fast Cross-Domain Singing Voice Conversion with Feature-wise Linear Modulation
Songxiang Liu, Yuewen Cao, Na Hu +2
This paper presents FastSVC, a light-weight cross-domain singing voice conversion (SVC) system, which can achieve high conversion performance, with inference speed 4x faster than r…
Learning Explicit Prosody Models and Deep Speaker Embeddings for Atypical Voice Conversion
Disong Wang, Songxiang Liu, Lifa Sun +3
Though significant progress has been made for the voice conversion (VC) of typical speech, VC for atypical speech, e.g., dysarthric and second-language (L2) speech, remains a chall…
Any-to-Many Voice Conversion with Location-Relative Sequence-to-Sequence Modeling
Songxiang Liu, Yuewen Cao, Disong Wang +3
This paper proposes an any-to-many location-relative, sequence-to-sequence (seq2seq), non-parallel voice conversion approach, which utilizes text supervision during training. In th…
Transferring Source Style in Non-Parallel Voice Conversion
Songxiang Liu, Yuewen Cao, Shiyin Kang +5
Voice conversion (VC) techniques aim to modify speaker identity of an utterance while preserving the underlying linguistic information. Most VC approaches ignore modeling of the sp…
Multi-Target Emotional Voice Conversion With Neural Vocoders
Songxiang Liu, Yuewen Cao, Helen Meng
Emotional voice conversion (EVC) is one way to generate expressive synthetic speech. Previous approaches mainly focused on modeling one-to-one mapping, i.e., conversion from one em…
Emotional Voice Conversion With Cycle-consistent Adversarial Network
Songxiang Liu, Yuewen Cao, Helen Meng
Emotional Voice Conversion, or emotional VC, is a technique of converting speech from one emotion state into another one, keeping the basic linguistic information and speaker ident…
Defense against adversarial attacks on spoofing countermeasures of ASV
Haibin Wu, Songxiang Liu, Helen Meng +1
Various forefront countermeasure methods for automatic speaker verification (ASV) with considerable performance in anti-spoofing are proposed in the ASVspoof 2019 challenge. Howeve…
Multi-Layer Content Interaction Through Quaternion Product For Visual Question Answering
Lei Shi, Shijie Geng, Kai Shuang +4
Multi-modality fusion technologies have greatly improved the performance of neural network-based Video Description/Caption, Visual Question Answering (VQA) and Audio Visual Scene-a…
Adversarial Attacks on Spoofing Countermeasures of automatic speaker verification
Songxiang Liu, Haibin Wu, Hung-yi Lee +1
High-performance spoofing countermeasure systems for automatic speaker verification (ASV) have been proposed in the ASVspoof 2019 challenge. However, the robustness of such systems…