304 citations · 1k across the 30 of their papers we have counts for
30 papers
WASE: Learning When to Attend for Speaker Extraction in Cocktail Party Environments
Yunzhe Hao, Jiaming Xu, Peng Zhang +1
In the speaker extraction problem, it is found that additional information from the target speaker contributes to the tracking and extraction of the target speaker, which includes…
MIMO Self-attentive RNN Beamformer for Multi-speaker Speech Separation
Xiyun Li, Yong Xu, Meng Yu +4
Recently, our proposed recurrent neural network (RNN) based all deep learning minimum variance distortionless response (ADL-MVDR) beamformer method yielded superior performance ove…
Speaker and Direction Inferred Dual-channel Speech Separation
Chenxing Li, Jiaming Xu, Nima Mesgarani +1
Most speech separation methods, trying to separate all channel sources simultaneously, are still far from having enough general- ization capabilities for real scenarios where the n…
Exploring wav2vec 2.0 on speaker verification and language identification
Zhiyun Fan, Meng Li, Shiyu Zhou +1
Wav2vec 2.0 is a recently proposed self-supervised framework for speech representation learning. It follows a two-stage training process of pre-training and fine-tuning, and perfor…
CIF-based Collaborative Decoding for End-to-end Contextual Speech Recognition
Minglun Han, Linhao Dong, Shiyu Zhou +1
End-to-end (E2E) models have achieved promising results on multiple speech recognition benchmarks, and shown the potential to become the mainstream. However, the unified structure…
Audio-visual Speech Separation with Adversarially Disentangled Visual Representation
Peng Zhang, Jiaming Xu, Jing shi +2
Speech separation aims to separate individual voice from an audio mixture of multiple simultaneous talkers. Although audio-only approaches achieve satisfactory performance, they bu…
Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals
Jing Shi, Xuankai Chang, Pengcheng Guo +5
Neural sequence-to-sequence models are well established for applications which can be cast as mapping a single input sequence into a single output sequence. In this work, we focus…
Speaker-Conditional Chain Model for Speech Separation and Extraction
Jing Shi, Jiaming Xu, Yusuke Fujita +2
Speech separation has been extensively explored to tackle the cocktail party problem. However, these studies are still far from having enough generalization capabilities for real s…
Speaker-aware speech-transformer
Zhiyun Fan, Jie Li, Shiyu Zhou +1
Recently, end-to-end (E2E) models become a competitive alternative to the conventional hybrid automatic speech recognition (ASR) systems. However, they still suffer from speaker mi…
DMRM: A Dual-channel Multi-hop Reasoning Model for Visual Dialog
Feilong Chen, Fandong Meng, Jiaming Xu +3
Visual Dialog is a vision-language task that requires an AI agent to engage in a conversation with humans grounded in an image. It remains a challenging task since it requires the…
Few-Features Attack to Fool Machine Learning Models through Mask-Based GAN
Feng Chen, Yunkai Shang, Bo Xu +1
GAN is a deep-learning based generative approach to generate contents such as images, languages and speeches. Recently, studies have shown that GAN can also be applied to generativ…
Unsupervised pre-training for sequence to sequence speech recognition
Zhiyun Fan, Shiyu Zhou, Bo Xu
This paper proposes a novel approach to pre-train encoder-decoder sequence-to-sequence (seq2seq) model with unpaired speech and transcripts respectively. Our pre-training method is…
CIF: Continuous Integrate-and-Fire for End-to-End Speech Recognition
Linhao Dong, Bo Xu
In this paper, we propose a novel soft and monotonic alignment mechanism used for sequence transduction. It is inspired by the integrate-and-fire model in spiking neural networks a…
Self-Attention Aligner: A Latency-Control End-to-End Model for ASR Using Self-Attention Network and Chunk-Hopping
Linhao Dong, Feng Wang, Bo Xu
Self-attention network, an attention-based feedforward neural network, has recently shown the potential to replace recurrent neural networks (RNNs) in a variety of NLP tasks. Howev…
A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule
Yunzhe Hao, Xuhui Huang, Meng Dong +1
Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are n…
Concept Learning through Deep Reinforcement Learning with Memory-Augmented Neural Networks
Jing Shi, Jiaming Xu, Yiqun Yao +1
Deep neural networks have shown superior performance in many regimes to remember familiar patterns with large amounts of data. However, the standard supervised deep learning paradi…
Cascaded Mutual Modulation for Visual Reasoning
Yiqun Yao, Jiaming Xu, Feng Wang +1
Visual reasoning is a special visual question answering problem that is multi-step and compositional by nature, and also requires intensive text-vision interactions. We propose CMM…
Single-channel Speech Dereverberation via Generative Adversarial Training
Chenxing Li, Tieqiang Wang, Shuang Xu +1
In this paper, we propose a single-channel speech dereverberation system (DeReGAT) based on convolutional, bidirectional long short-term memory and deep feed-forward neural network…
Extending Recurrent Neural Aligner for Streaming End-to-End Speech Recognition in Mandarin
Linhao Dong, Shiyu Zhou, Wei Chen +1
End-to-end models have been showing superiority in Automatic Speech Recognition (ASR). At the same time, the capacity of streaming recognition has become a growing requirement for…
NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition
Fenfen Sheng, Zhineng Chen, Bo Xu
Scene text recognition has attracted a great many researches due to its importance to various applications. Existing methods mainly adopt recurrence or convolution based networks.…
A Comparison of Modeling Units in Sequence-to-Sequence Speech Recognition with the Transformer on Mandarin Chinese
Shiyu Zhou, Linhao Dong, Shuang Xu +1
The choice of modeling units is critical to automatic speech recognition (ASR) tasks. Conventional ASR systems typically choose context-dependent states (CD-states) or context-depe…
Unsupervised Neural Machine Translation with Weight Sharing
Zhen Yang, Wei Chen, Feng Wang +1
Unsupervised neural machine translation (NMT) is a recently proposed approach for machine translation which aims to train the model without using any labeled data. The models propo…
Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin Chinese
Shiyu Zhou, Linhao Dong, Shuang Xu +1
Sequence-to-sequence attention-based models have recently shown very promising results on automatic speech recognition (ASR) tasks, which integrate an acoustic, pronunciation and l…
Convolutional Neural Network with Word Embeddings for Chinese Word Segmentation
Chunqi Wang, Bo Xu
Character-based sequence labeling framework is flexible and efficient for Chinese word segmentation (CWS). Recently, many character-based neural models have been applied to CWS. Wh…