93 citations · 465 across the 63 of their papers we have counts for
11 papers · 1 filter
Exploiting Audio-Visual Features with Pretrained AV-HuBERT for Multi-Modal Dysarthric Speech Reconstruction
Xueyuan Chen, Yuejiao Wang, Xixin Wu +4
Dysarthric speech reconstruction (DSR) aims to transform dysarthric speech into normal speech by improving the intelligibility and naturalness. This is a challenging task especiall…
Cross-Speaker Encoding Network for Multi-Talker Speech Recognition
Jiawen Kang, Lingwei Meng, Mingyu Cui +4
End-to-end multi-talker speech recognition has garnered great interest as an effective approach to directly transcribe overlapped speech from multiple speakers. Current methods typ…
Exploring Self-supervised Pre-trained ASR Models For Dysarthric and Elderly Speech Recognition
Shujie Hu, Xurong Xie, Zengrui Jin +6
Automatic recognition of disordered and elderly speech remains a highly challenging task to date due to the difficulty in collecting such data in large quantities. This paper explo…
Towards Green ASR: Lossless 4-bit Quantization of a Hybrid TDNN System on the 300-hr Switchboard Corpus
Junhao Xu, Shoukang Hu, Xunying Liu +1
State of the art time automatic speech recognition (ASR) systems are becoming increasingly complex and expensive for practical applications. This paper presents the development of…
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…
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…
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…
Mixed Precision DNN Qunatization for Overlapped Speech Separation and Recognition
Junhao Xu, Jianwei Yu, Xunying Liu +1
Recognition of overlapped speech has been a highly challenging task to date. State-of-the-art multi-channel speech separation system are becoming increasingly complex and expensive…
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…
Bayesian Learning for Deep Neural Network Adaptation
Xurong Xie, Xunying Liu, Tan Lee +1
A key task for speech recognition systems is to reduce the mismatch between training and evaluation data that is often attributable to speaker differences. Speaker adaptation techn…