93 citations · 526 across the 105 of their papers we have counts for
41 papers · 1 filter
learning discriminative features from spectrograms using center loss for speech emotion recognition
Dongyang Dai, Zhiyong Wu, Runnan Li +3
Identifying the emotional state from speech is essential for the natural interaction of the machine with the speaker. However, extracting effective features for emotion recognition…
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…
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…
Speech Representation Disentanglement with Adversarial Mutual Information Learning for One-shot Voice Conversion
SiCheng Yang, Methawee Tantrawenith, Haolin Zhuang +8
One-shot voice conversion (VC) with only a single target speaker's speech for reference has become a hot research topic. Existing works generally disentangle timbre, while informat…
Exploiting Cross-domain And Cross-Lingual Ultrasound Tongue Imaging Features For Elderly And Dysarthric Speech Recognition
Shujie Hu, Xurong Xie, Mengzhe Geng +6
Articulatory features are inherently invariant to acoustic signal distortion and have been successfully incorporated into automatic speech recognition (ASR) systems designed for no…
Exploiting Cross Domain Acoustic-to-articulatory Inverted Features For Disordered Speech Recognition
Shujie Hu, Shansong Liu, Xurong Xie +6
Articulatory features are inherently invariant to acoustic signal distortion and have been successfully incorporated into automatic speech recognition (ASR) systems for normal spee…
Speaker Adaptation Using Spectro-Temporal Deep Features for Dysarthric and Elderly Speech Recognition
Mengzhe Geng, Xurong Xie, Zi Ye +5
Despite the rapid progress of automatic speech recognition (ASR) technologies targeting normal speech in recent decades, accurate recognition of dysarthric and elderly speech remai…
Recent Progress in the CUHK Dysarthric Speech Recognition System
Shansong Liu, Mengzhe Geng, Shoukang Hu +5
Despite the rapid progress of automatic speech recognition (ASR) technologies in the past few decades, recognition of disordered speech remains a highly challenging task to date. D…
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…
VCVTS: Multi-speaker Video-to-Speech synthesis via cross-modal knowledge transfer from voice conversion
Disong Wang, Shan Yang, Dan Su +3
Though significant progress has been made for speaker-dependent Video-to-Speech (VTS) synthesis, little attention is devoted to multi-speaker VTS that can map silent video to speec…
Partially Fake Audio Detection by Self-attention-based Fake Span Discovery
Haibin Wu, Heng-Cheng Kuo, Naijun Zheng +5
The past few years have witnessed the significant advances of speech synthesis and voice conversion technologies. However, such technologies can undermine the robustness of broadly…
The CUHK-TENCENT speaker diarization system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge
Naijun Zheng, Na Li, Xixin Wu +6
This paper describes our speaker diarization system submitted to the Multi-channel Multi-party Meeting Transcription (M2MeT) challenge, where Mandarin meeting data were recorded in…
Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks
Shoukang Hu, Xurong Xie, Mingyu Cui +6
State-of-the-art automatic speech recognition (ASR) system development is data and computation intensive. The optimal design of deep neural networks (DNNs) for these systems often…
Adversarial Data Augmentation for Disordered Speech Recognition
Zengrui Jin, Mengzhe Geng, Xurong Xie +4
Automatic recognition of disordered speech remains a highly challenging task to date. The underlying neuro-motor conditions, often compounded with co-occurring physical disabilitie…
Channel-wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attacks
Xu Li, Xixin Wu, Hui Lu +2
Existing approaches for anti-spoofing in automatic speaker verification (ASV) still lack generalizability to unseen attacks. The Res2Net approach designs a residual-like connection…
VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion
Disong Wang, Liqun Deng, Yu Ting Yeung +3
One-shot voice conversion (VC), which performs conversion across arbitrary speakers with only a single target-speaker utterance for reference, can be effectively achieved by speech…
Unsupervised Domain Adaptation for Dysarthric Speech Detection via Domain Adversarial Training and Mutual Information Minimization
Disong Wang, Liqun Deng, Yu Ting Yeung +3
Dysarthric speech detection (DSD) systems aim to detect characteristics of the neuromotor disorder from speech. Such systems are particularly susceptible to domain mismatch where t…
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…
Adversarial defense for automatic speaker verification by cascaded self-supervised learning models
Haibin Wu, Xu Li, Andy T. Liu +3
Automatic speaker verification (ASV) is one of the core technologies in biometric identification. With the ubiquitous usage of ASV systems in safety-critical applications, more and…
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…
Unsupervised Cross-Lingual Speech Emotion Recognition Using DomainAdversarial Neural Network
Xiong Cai, Zhiyong Wu, Kuo Zhong +3
By using deep learning approaches, Speech Emotion Recog-nition (SER) on a single domain has achieved many excellentresults. However, cross-domain SER is still a challenging taskdue…
Bayesian Learning of LF-MMI Trained Time Delay Neural Networks for Speech Recognition
Shoukang Hu, Xurong Xie, Shansong Liu +5
Discriminative training techniques define state-of-the-art performance for automatic speech recognition systems. However, they are inherently prone to overfitting, leading to poor…
Improved End-to-End Dysarthric Speech Recognition via Meta-learning Based Model Re-initialization
Disong Wang, Jianwei Yu, Xixin Wu +3
Dysarthric speech recognition is a challenging task as dysarthric data is limited and its acoustics deviate significantly from normal speech. Model-based speaker adaptation is a pr…
Audio-visual Multi-channel Integration and Recognition of Overlapped Speech
Jianwei Yu, Shi-Xiong Zhang, Bo Wu +6
Automatic speech recognition (ASR) technologies have been significantly advanced in the past few decades. However, recognition of overlapped speech remains a highly challenging tas…