303 citations · 697 across the 37 of their papers we have counts for
14 papers · 1 filter
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…
A Two-Stage Approach to Device-Robust Acoustic Scene Classification
Hu Hu, Chao-Han Huck Yang, Xianjun Xia +13
To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convol…
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition
Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen +4
We propose a novel decentralized feature extraction approach in federated learning to address privacy-preservation issues for speech recognition. It is built upon a quantum convolu…
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…
Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation
Hu Hu, Chao-Han Huck Yang, Xianjun Xia +13
In this technical report, we present a joint effort of four groups, namely GT, USTC, Tencent, and UKE, to tackle Task 1 - Acoustic Scene Classification (ASC) in the DCASE 2020 Chal…
On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression
Jun Qi, Jun Du, Sabato Marco Siniscalchi +2
In this paper, we exploit the properties of mean absolute error (MAE) as a loss function for the deep neural network (DNN) based vector-to-vector regression. The goal of this work…
Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network Based Vector-to-Vector Regression
Jun Qi, Jun Du, Sabato Marco Siniscalchi +2
In this paper, we show that, in vector-to-vector regression utilizing deep neural networks (DNNs), a generalized loss of mean absolute error (MAE) between the predicted and expecte…
Exploring Deep Hybrid Tensor-to-Vector Network Architectures for Regression Based Speech Enhancement
Jun Qi, Hu Hu, Yannan Wang +3
This paper investigates different trade-offs between the number of model parameters and enhanced speech qualities by employing several deep tensor-to-vector regression models for s…
Relational Teacher Student Learning with Neural Label Embedding for Device Adaptation in Acoustic Scene Classification
Hu Hu, Sabato Marco Siniscalchi, Yannan Wang +1
In this paper, we propose a domain adaptation framework to address the device mismatch issue in acoustic scene classification leveraging upon neural label embedding (NLE) and relat…
An Acoustic Segment Model Based Segment Unit Selection Approach to Acoustic Scene Classification with Partial Utterances
Hu Hu, Sabato Marco Siniscalchi, Yannan Wang +3
In this paper, we propose a sub-utterance unit selection framework to remove acoustic segments in audio recordings that carry little information for acoustic scene classification (…
L-Vector: Neural Label Embedding for Domain Adaptation
Zhong Meng, Hu Hu, Jinyu Li +4
We propose a novel neural label embedding (NLE) scheme for the domain adaptation of a deep neural network (DNN) acoustic model with unpaired data samples from source and target dom…
Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement
Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen +2
Recent studies have highlighted adversarial examples as ubiquitous threats to the deep neural network (DNN) based speech recognition systems. In this work, we present a U-Net based…
Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning
Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen +4
Recent deep neural networks based techniques, especially those equipped with the ability of self-adaptation in the system level such as deep reinforcement learning (DRL), are shown…
Tensor-to-Vector Regression for Multi-channel Speech Enhancement based on Tensor-Train Network
Jun Qi, Hu Hu, Yannan Wang +3
We propose a tensor-to-vector regression approach to multi-channel speech enhancement in order to address the issue of input size explosion and hidden-layer size expansion. The key…