85 citations · 632 across the 65 of their papers we have counts for
13 papers · 1 filter
Tucker decomposition-based Temporal Knowledge Graph Completion
Pengpeng Shao, Guohua Yang, Dawei Zhang +3
Knowledge graphs have been demonstrated to be an effective tool for numerous intelligent applications. However, a large amount of valuable knowledge still exists implicitly in the…
Self-supervised Graph Representation Learning via Bootstrapping
Feihu Che, Guohua Yang, Dawei Zhang +3
Graph neural networks~(GNNs) apply deep learning techniques to graph-structured data and have achieved promising performance in graph representation learning. However, existing GNN…
Deep Time Delay Neural Network for Speech Enhancement with Full Data Learning
Cunhang Fan, Bin Liu, Jianhua Tao +3
Recurrent neural networks (RNNs) have shown significant improvements in recent years for speech enhancement. However, the model complexity and inference time cost of RNNs are much…
Gated Recurrent Fusion with Joint Training Framework for Robust End-to-End Speech Recognition
Cunhang Fan, Jiangyan Yi, Jianhua Tao +3
The joint training framework for speech enhancement and recognition methods have obtained quite good performances for robust end-to-end automatic speech recognition (ASR). However,…
Decoupling Pronunciation and Language for End-to-end Code-switching Automatic Speech Recognition
Shuai Zhang, Jiangyan Yi, Zhengkun Tian +3
Despite the recent significant advances witnessed in end-to-end (E2E) ASR system for code-switching, hunger for audio-text paired data limits the further improvement of the models'…
One In A Hundred: Select The Best Predicted Sequence from Numerous Candidates for Streaming Speech Recognition
Zhengkun Tian, Jiangyan Yi, Ye Bai +3
The RNN-Transducers and improved attention-based encoder-decoder models are widely applied to streaming speech recognition. Compared with these two end-to-end models, the CTC model…
Spike-Triggered Non-Autoregressive Transformer for End-to-End Speech Recognition
Zhengkun Tian, Jiangyan Yi, Jianhua Tao +3
Non-autoregressive transformer models have achieved extremely fast inference speed and comparable performance with autoregressive sequence-to-sequence models in neural machine tran…
Listen Attentively, and Spell Once: Whole Sentence Generation via a Non-Autoregressive Architecture for Low-Latency Speech Recognition
Ye Bai, Jiangyan Yi, Jianhua Tao +3
Although attention based end-to-end models have achieved promising performance in speech recognition, the multi-pass forward computation in beam-search increases inference time cos…
Simultaneous Denoising and Dereverberation Using Deep Embedding Features
Cunhang Fan, Jianhua Tao, Bin Liu +2
Monaural speech dereverberation is a very challenging task because no spatial cues can be used. When the additive noises exist, this task becomes more challenging. In this paper, w…
Adversarial Transfer Learning for Punctuation Restoration
Jiangyan Yi, Jianhua Tao, Ye Bai +2
Previous studies demonstrate that word embeddings and part-of-speech (POS) tags are helpful for punctuation restoration tasks. However, two drawbacks still exist. One is that word…
Deep Attention Fusion Feature for Speech Separation with End-to-End Post-filter Method
Cunhang Fan, Jianhua Tao, Bin Liu +3
In this paper, we propose an end-to-end post-filter method with deep attention fusion features for monaural speaker-independent speech separation. At first, a time-frequency domain…
Rnn-transducer with language bias for end-to-end Mandarin-English code-switching speech recognition
Shuai Zhang, Jiangyan Yi, Zhengkun Tian +2
Recently, language identity information has been utilized to improve the performance of end-to-end code-switching (CS) speech recognition. However, previous works use an additional…
Spatial and spectral deep attention fusion for multi-channel speech separation using deep embedding features
Cunhang Fan, Bin Liu, Jianhua Tao +2
Multi-channel deep clustering (MDC) has acquired a good performance for speech separation. However, MDC only applies the spatial features as the additional information. So it is di…