94 citations · 129 across the 21 of their papers we have counts for
21 papers
SemaVoice: Semantic-Aware Continuous Autoregressive Speech Synthesis
Huimeng Wang, Hui Lu, Jiajun Deng +7
Continuous autoregressive speech synthesis has recently emerged as a promising direction for zero-shot text-to-speech (TTS). However, existing methods still suffer from a fundament…
Disambiguation of Chinese Polyphones in an End-to-End Framework with Semantic Features Extracted by Pre-trained BERT
Dongyang Dai, Zhiyong Wu, Shiyin Kang +5
Grapheme-to-phoneme (G2P) conversion serves as an essential component in Chinese Mandarin text-to-speech (TTS) system, where polyphone disambiguation is the core issue. In this pap…
Improving Language Model-Based Zero-Shot Text-to-Speech Synthesis with Multi-Scale Acoustic Prompts
Shun Lei, Yixuan Zhou, Liyang Chen +8
Zero-shot text-to-speech (TTS) synthesis aims to clone any unseen speaker's voice without adaptation parameters. By quantizing speech waveform into discrete acoustic tokens and mod…
Towards Improving the Expressiveness of Singing Voice Synthesis with BERT Derived Semantic Information
Shaohuan Zhou, Shun Lei, Weiya You +5
This paper presents an end-to-end high-quality singing voice synthesis (SVS) system that uses bidirectional encoder representation from Transformers (BERT) derived semantic embeddi…
Towards Spontaneous Style Modeling with Semi-supervised Pre-training for Conversational Text-to-Speech Synthesis
Weiqin Li, Shun Lei, Qiaochu Huang +4
The spontaneous behavior that often occurs in conversations makes speech more human-like compared to reading-style. However, synthesizing spontaneous-style speech is challenging du…
Improving Mandarin Prosodic Structure Prediction with Multi-level Contextual Information
Jie Chen, Changhe Song, Deyi Tuo +4
For text-to-speech (TTS) synthesis, prosodic structure prediction (PSP) plays an important role in producing natural and intelligible speech. Although inter-utterance linguistic in…
MSStyleTTS: Multi-Scale Style Modeling with Hierarchical Context Information for Expressive Speech Synthesis
Shun Lei, Yixuan Zhou, Liyang Chen +4
Expressive speech synthesis is crucial for many human-computer interaction scenarios, such as audiobooks, podcasts, and voice assistants. Previous works focus on predicting the sty…
GTN-Bailando: Genre Consistent Long-Term 3D Dance Generation based on Pre-trained Genre Token Network
Haolin Zhuang, Shun Lei, Long Xiao +6
Music-driven 3D dance generation has become an intensive research topic in recent years with great potential for real-world applications. Most existing methods lack the considerati…
CB-Conformer: Contextual biasing Conformer for biased word recognition
Yaoxun Xu, Baiji Liu, Qiaochu Huang and +4
Due to the mismatch between the source and target domains, how to better utilize the biased word information to improve the performance of the automatic speech recognition model in…
Context-aware Coherent Speaking Style Prediction with Hierarchical Transformers for Audiobook Speech Synthesis
Shun Lei, Yixuan Zhou, Liyang Chen +3
Recent advances in text-to-speech have significantly improved the expressiveness of synthesized speech. However, it is still challenging to generate speech with contextually approp…
Towards Expressive Speaking Style Modelling with Hierarchical Context Information for Mandarin Speech Synthesis
Shun Lei, Yixuan Zhou, Liyang Chen +3
Previous works on expressive speech synthesis mainly focus on current sentence. The context in adjacent sentences is neglected, resulting in inflexible speaking style for the same…
Towards Multi-Scale Speaking Style Modelling with Hierarchical Context Information for Mandarin Speech Synthesis
Shun Lei, Yixuan Zhou, Liyang Chen +4
Previous works on expressive speech synthesis focus on modelling the mono-scale style embedding from the current sentence or context, but the multi-scale nature of speaking style i…
FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for Speech Enhancement
Jun Chen, Zilin Wang, Deyi Tuo +3
Previously proposed FullSubNet has achieved outstanding performance in Deep Noise Suppression (DNS) Challenge and attracted much attention. However, it still encounters issues such…
Disentangleing Content and Fine-grained Prosody Information via Hybrid ASR Bottleneck Features for Voice Conversion
Xintao Zhao, Feng Liu, Changhe Song +4
Non-parallel data voice conversion (VC) have achieved considerable breakthroughs recently through introducing bottleneck features (BNFs) extracted by the automatic speech recogniti…
VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis
Hui Lu, Zhiyong Wu, Xixin Wu +4
This paper describes a variational auto-encoder based non-autoregressive text-to-speech (VAENAR-TTS) model. The autoregressive TTS (AR-TTS) models based on the sequence-to-sequence…
Adversarially learning disentangled speech representations for robust multi-factor voice conversion
Jie Wang, Jingbei Li, Xintao Zhao +3
Factorizing speech as disentangled speech representations is vital to achieve highly controllable style transfer in voice conversion (VC). Conventional speech representation learni…
Speaker Independent and Multilingual/Mixlingual Speech-Driven Talking Head Generation Using Phonetic Posteriorgrams
Huirong Huang, Zhiyong Wu, Shiyin Kang +9
Generating 3D speech-driven talking head has received more and more attention in recent years. Recent approaches mainly have following limitations: 1) most speaker-independent meth…
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…
Audio-visual Recognition of Overlapped speech for the LRS2 dataset
Jianwei Yu, Shi-Xiong Zhang, Jian Wu +7
Automatic recognition of overlapped speech remains a highly challenging task to date. Motivated by the bimodal nature of human speech perception, this paper investigates the use of…
DurIAN: Duration Informed Attention Network For Multimodal Synthesis
Chengzhu Yu, Heng Lu, Na Hu +9
In this paper, we present a generic and robust multimodal synthesis system that produces highly natural speech and facial expression simultaneously. The key component of this syste…
Maximizing Mutual Information for Tacotron
Peng Liu, Xixin Wu, Shiyin Kang +3
End-to-end speech synthesis methods already achieve close-to-human quality performance. However compared to HMM-based and NN-based frame-to-frame regression methods, they are prone…