105 citations · 715 across the 41 of their papers we have counts for
41 papers
CASA-ASR: Context-Aware Speaker-Attributed ASR
Mohan Shi, Zhihao Du, Qian Chen +5
Recently, speaker-attributed automatic speech recognition (SA-ASR) has attracted a wide attention, which aims at answering the question ``who spoke what''. Different from modular s…
Semantic VAD: Low-Latency Voice Activity Detection for Speech Interaction
Mohan Shi, Yuchun Shu, Lingyun Zuo +4
For speech interaction, voice activity detection (VAD) is often used as a front-end. However, traditional VAD algorithms usually need to wait for a continuous tail silence to reach…
Joint Generative-Contrastive Representation Learning for Anomalous Sound Detection
Xiao-Min Zeng, Yan Song, Zhu Zhuo +5
In this paper, we propose a joint generative and contrastive representation learning method (GeCo) for anomalous sound detection (ASD). GeCo exploits a Predictive AutoEncoder (PAE)…
AST-SED: An Effective Sound Event Detection Method Based on Audio Spectrogram Transformer
Kang Li, Yan Song, Li-Rong Dai +3
In this paper, we propose an effective sound event detection (SED) method based on the audio spectrogram transformer (AST) model, pretrained on the large-scale AudioSet for audio t…
A Comparative Study on Multichannel Speaker-Attributed Automatic Speech Recognition in Multi-party Meetings
Mohan Shi, Jie Zhang, Zhihao Du +4
Speaker-attributed automatic speech recognition (SA-ASR) in multi-party meeting scenarios is one of the most valuable and challenging ASR task. It was shown that single-channel fra…
Robust Data2vec: Noise-robust Speech Representation Learning for ASR by Combining Regression and Improved Contrastive Learning
Qiu-Shi Zhu, Long Zhou, Jie Zhang +3
Self-supervised pre-training methods based on contrastive learning or regression tasks can utilize more unlabeled data to improve the performance of automatic speech recognition (A…
Joint Training of Speech Enhancement and Self-supervised Model for Noise-robust ASR
Qiu-Shi Zhu, Jie Zhang, Zi-Qiang Zhang +1
Speech enhancement (SE) is usually required as a front end to improve the speech quality in noisy environments, while the enhanced speech might not be optimal for automatic speech…
MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services
Dianhai Yu, Liang Shen, Hongxiang Hao +5
While modern internet services, such as chatbots, search engines, and online advertising, demand the use of large-scale deep neural networks (DNNs), distributed training and infere…
A Complementary Joint Training Approach Using Unpaired Speech and Text for Low-Resource Automatic Speech Recognition
Ye-Qian Du, Jie Zhang, Qiu-Shi Zhu +4
Unpaired data has shown to be beneficial for low-resource automatic speech recognition~(ASR), which can be involved in the design of hybrid models with multi-task training or langu…
Learning Contextually Fused Audio-visual Representations for Audio-visual Speech Recognition
Zi-Qiang Zhang, Jie Zhang, Jian-Shu Zhang +3
With the advance in self-supervised learning for audio and visual modalities, it has become possible to learn a robust audio-visual speech representation. This would be beneficial…
Supervised and Self-supervised Pretraining Based COVID-19 Detection Using Acoustic Breathing/Cough/Speech Signals
Xing-Yu Chen, Qiu-Shi Zhu, Jie Zhang +1
In this work, we propose a bi-directional long short-term memory (BiLSTM) network based COVID-19 detection method using breath/speech/cough signals. By using the acoustic signals t…
A Noise-Robust Self-supervised Pre-training Model Based Speech Representation Learning for Automatic Speech Recognition
Qiu-Shi Zhu, Jie Zhang, Zi-Qiang Zhang +3
Wav2vec2.0 is a popular self-supervised pre-training framework for learning speech representations in the context of automatic speech recognition (ASR). It was shown that wav2vec2.…
The USTC-NELSLIP Systems for Simultaneous Speech Translation Task at IWSLT 2021
Dan Liu, Mengge Du, Xiaoxi Li +2
This paper describes USTC-NELSLIP's submissions to the IWSLT2021 Simultaneous Speech Translation task. We proposed a novel simultaneous translation model, Cross Attention Augmented…
XLST: Cross-lingual Self-training to Learn Multilingual Representation for Low Resource Speech Recognition
Zi-Qiang Zhang, Yan Song, Ming-Hui Wu +2
In this paper, we propose a weakly supervised multilingual representation learning framework, called cross-lingual self-training (XLST). XLST is able to utilize a small amount of a…
Lip-reading with Hierarchical Pyramidal Convolution and Self-Attention
Hang Chen, Jun Du, Yu Hu +3
In this paper, we propose a novel deep learning architecture to improving word-level lip-reading. On the one hand, we first introduce the multi-scale processing into the spatial fe…
Correlating Subword Articulation with Lip Shapes for Embedding Aware Audio-Visual Speech Enhancement
Hang Chen, Jun Du, Yu Hu +3
In this paper, we propose a visual embedding approach to improving embedding aware speech enhancement (EASE) by synchronizing visual lip frames at the phone and place of articulati…
Voice Conversion by Cascading Automatic Speech Recognition and Text-to-Speech Synthesis with Prosody Transfer
Jing-Xuan Zhang, Li-Juan Liu, Yan-Nian Chen +4
With the development of automatic speech recognition (ASR) and text-to-speech synthesis (TTS) technique, it's intuitive to construct a voice conversion system by cascading an ASR a…
Attentive Fusion Enhanced Audio-Visual Encoding for Transformer Based Robust Speech Recognition
Liangfa Wei, Jie Zhang, Junfeng Hou +1
Audio-visual information fusion enables a performance improvement in speech recognition performed in complex acoustic scenarios, e.g., noisy environments. It is required to explore…
Recognition-Synthesis Based Non-Parallel Voice Conversion with Adversarial Learning
Jing-Xuan Zhang, Zhen-Hua Ling, Li-Rong Dai
This paper presents an adversarial learning method for recognition-synthesis based non-parallel voice conversion. A recognizer is used to transform acoustic features into linguisti…
Attentive batch normalization for lstm-based acoustic modeling of speech recognition
Fenglin Ding, Wu Guo, Lirong Dai +1
Batch normalization (BN) is an effective method to accelerate model training and improve the generalization performance of neural networks. In this paper, we propose an improved ba…
Attention-based gated scaling adaptative acoustic model for ctc-based speech recognition
Fenglin Ding, Wu Guo, Lirong Dai +1
In this paper, we propose a novel adaptive technique that uses an attention-based gated scaling (AGS) scheme to improve deep feature learning for connectionist temporal classificat…
End-to-End Emotional Speech Synthesis Using Style Tokens and Semi-Supervised Training
Peng-fei Wu, Zhen-hua Ling, Li-juan Liu +3
This paper proposes an end-to-end emotional speech synthesis (ESS) method which adopts global style tokens (GSTs) for semi-supervised training. This model is built based on the GST…
Singing Voice Synthesis Using Deep Autoregressive Neural Networks for Acoustic Modeling
Yuan-Hao Yi, Yang Ai, Zhen-Hua Ling +1
This paper presents a method of using autoregressive neural networks for the acoustic modeling of singing voice synthesis (SVS). Singing voice differs from speech and it contains m…
Non-Parallel Sequence-to-Sequence Voice Conversion with Disentangled Linguistic and Speaker Representations
Jing-Xuan Zhang, Zhen-Hua Ling, Li-Rong Dai
This paper presents a method of sequence-to-sequence (seq2seq) voice conversion using non-parallel training data. In this method, disentangled linguistic and speaker representation…