93 citations · 526 across the 105 of their papers we have counts for
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SememeASR: Boosting Performance of End-to-End Speech Recognition against Domain and Long-Tailed Data Shift with Sememe Semantic Knowledge
Jiaxu Zhu, Changhe Song, Zhiyong Wu +1
Recently, excellent progress has been made in speech recognition. However, pure data-driven approaches have struggled to solve the problem in domain-mismatch and long-tailed data.…
Enhancing the vocal range of single-speaker singing voice synthesis with melody-unsupervised pre-training
Shaohuan Zhou, Xu Li, Zhiyong Wu +2
The single-speaker singing voice synthesis (SVS) usually underperforms at pitch values that are out of the singer's vocal range or associated with limited training samples. Based o…
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…
CALM: Contrastive Cross-modal Speaking Style Modeling for Expressive Text-to-Speech Synthesis
Yi Meng, Xiang Li, Zhiyong Wu +6
To further improve the speaking styles of synthesized speeches, current text-to-speech (TTS) synthesis systems commonly employ reference speeches to stylize their outputs instead o…
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…
The defender's perspective on automatic speaker verification: An overview
Haibin Wu, Jiawen Kang, Lingwei Meng +2
Automatic speaker verification (ASV) plays a critical role in security-sensitive environments. Regrettably, the reliability of ASV has been undermined by the emergence of spoofing…
Adversarial Speaker Disentanglement Using Unannotated External Data for Self-supervised Representation Based Voice Conversion
Xintao Zhao, Shuai Wang, Yang Chao +2
Nowadays, recognition-synthesis-based methods have been quite popular with voice conversion (VC). By introducing linguistics features with good disentangling characters extracted f…
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…
Joint Multi-scale Cross-lingual Speaking Style Transfer with Bidirectional Attention Mechanism for Automatic Dubbing
Jingbei Li, Sipan Li, Ping Chen +7
Automatic dubbing, which generates a corresponding version of the input speech in another language, could be widely utilized in many real-world scenarios such as video and game loc…
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…
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)…
Towards High-Quality Neural TTS for Low-Resource Languages by Learning Compact Speech Representations
Haohan Guo, Fenglong Xie, Xixin Wu +2
This paper aims to enhance low-resource TTS by reducing training data requirements using compact speech representations. A Multi-Stage Multi-Codebook (MSMC) VQ-GAN is trained to le…
Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detection
Xuanjun Chen, Haibin Wu, Helen Meng +2
Audio-visual active speaker detection (AVASD) is well-developed, and now is an indispensable front-end for several multi-modal applications. However, to the best of our knowledge,…
Towards Cross-speaker Reading Style Transfer on Audiobook Dataset
Xiang Li, Changhe Song, Xianhao Wei +3
Cross-speaker style transfer aims to extract the speech style of the given reference speech, which can be reproduced in the timbre of arbitrary target speakers. Existing methods on…
Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusion
Haibin Wu, Jiawen Kang, Lingwei Meng +5
Recent years have witnessed the extraordinary development of automatic speaker verification (ASV). However, previous works show that state-of-the-art ASV models are seriously vulne…
Audio-visual multi-channel speech separation, dereverberation and recognition
Guinan Li, Jianwei Yu, Jiajun Deng +2
Despite the rapid advance of automatic speech recognition (ASR) technologies, accurate recognition of cocktail party speech characterised by the interference from overlapping speak…
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…
Content-Dependent Fine-Grained Speaker Embedding for Zero-Shot Speaker Adaptation in Text-to-Speech Synthesis
Yixuan Zhou, Changhe Song, Xiang Li +5
Zero-shot speaker adaptation aims to clone an unseen speaker's voice without any adaptation time and parameters. Previous researches usually use a speaker encoder to extract a glob…
Neural Architecture Search for Speech Emotion Recognition
Xixin Wu, Shoukang Hu, Zhiyong Wu +2
Deep neural networks have brought significant advancements to speech emotion recognition (SER). However, the architecture design in SER is mainly based on expert knowledge and empi…