93 citations · 187 across the 13 of their papers we have counts for
13 papers
Music Understanding LLaMA: Advancing Text-to-Music Generation with Question Answering and Captioning
Shansong Liu, Atin Sakkeer Hussain, Chenshuo Sun +1
Text-to-music generation (T2M-Gen) faces a major obstacle due to the scarcity of large-scale publicly available music datasets with natural language captions. To address this, we p…
A Hierarchical Speaker Representation Framework for One-shot Singing Voice Conversion
Xu Li, Shansong Liu, Ying Shan
Typically, singing voice conversion (SVC) depends on an embedding vector, extracted from either a speaker lookup table (LUT) or a speaker recognition network (SRN), to model speake…
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…
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…
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…
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…
Bayesian Transformer Language Models for Speech Recognition
Boyang Xue, Jianwei Yu, Junhao Xu +6
State-of-the-art neural language models (LMs) represented by Transformers are highly complex. Their use of fixed, deterministic parameter estimates fail to account for model uncert…
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…
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…
Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks
Shoukang Hu, Xurong Xie, Shansong Liu +4
Deep neural networks (DNNs) based automatic speech recognition (ASR) systems are often designed using expert knowledge and empirical evaluation. In this paper, a range of neural ar…
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…