93 citations · 526 across the 105 of their papers we have counts for
17 papers · 2 filters
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…
NeuFA: Neural Network Based End-to-End Forced Alignment with Bidirectional Attention Mechanism
Jingbei Li, Yi Meng, Zhiyong Wu +4
Although deep learning and end-to-end models have been widely used and shown superiority in automatic speech recognition (ASR) and text-to-speech (TTS) synthesis, state-of-the-art…
Spoofing-Aware Speaker Verification by Multi-Level Fusion
Haibin Wu, Lingwei Meng, Jiawen Kang +5
Recently, many novel techniques have been introduced to deal with spoofing attacks, and achieve promising countermeasure (CM) performances. However, these works only take the stand…
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…
A Multi-Scale Time-Frequency Spectrogram Discriminator for GAN-based Non-Autoregressive TTS
Haohan Guo, Hui Lu, Xixin Wu +1
The generative adversarial network (GAN) has shown its outstanding capability in improving Non-Autoregressive TTS (NAR-TTS) by adversarially training it with an extra model that di…
MFA-Conformer: Multi-scale Feature Aggregation Conformer for Automatic Speaker Verification
Yang Zhang, Zhiqiang Lv, Haibin Wu +5
In this paper, we present Multi-scale Feature Aggregation Conformer (MFA-Conformer), an easy-to-implement, simple but effective backbone for automatic speaker verification based on…
Investigation of Data Augmentation Techniques for Disordered Speech Recognition
Mengzhe Geng, Xurong Xie, Shansong Liu +4
Disordered speech recognition is a highly challenging task. The underlying neuro-motor conditions of people with speech disorders, often compounded with co-occurring physical disab…
Spectro-Temporal Deep Features for Disordered Speech Assessment and Recognition
Mengzhe Geng, Shansong Liu, Jianwei Yu +6
Automatic recognition of disordered speech remains a highly challenging task to date. Sources of variability commonly found in normal speech including accent, age or gender, when f…