303 citations · 697 across the 37 of their papers we have counts for
13 papers · 1 filter
A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition
Chao-Han Huck Yang, Bo Li, Yu Zhang +4
We propose a quantum kernel learning (QKL) framework to address the inherent data sparsity issues often encountered in training large-scare acoustic models in low-resource scenario…
An Experimental Study on Private Aggregation of Teacher Ensemble Learning for End-to-End Speech Recognition
Chao-Han Huck Yang, I-Fan Chen, Andreas Stolcke +2
Differential privacy (DP) is one data protection avenue to safeguard user information used for training deep models by imposing noisy distortion on privacy data. Such a noise pertu…
A Study of Designing Compact Audio-Visual Wake Word Spotting System Based on Iterative Fine-Tuning in Neural Network Pruning
Hengshun Zhou, Jun Du, Chao-Han Huck Yang +2
Audio-only-based wake word spotting (WWS) is challenging under noisy conditions due to environmental interference in signal transmission. In this paper, we investigate on designing…
Information Fusion in Attention Networks Using Adaptive and Multi-level Factorized Bilinear Pooling for Audio-visual Emotion Recognition
Hengshun Zhou, Jun Du, Yuanyuan Zhang +3
Multimodal emotion recognition is a challenging task in emotion computing as it is quite difficult to extract discriminative features to identify the subtle differences in human em…
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification
Hao Yen, Chao-Han Huck Yang, Hu Hu +9
We propose a novel neural model compression strategy combining data augmentation, knowledge transfer, pruning, and quantization for device-robust acoustic scene classification (ASC…
PATE-AAE: Incorporating Adversarial Autoencoder into Private Aggregation of Teacher Ensembles for Spoken Command Classification
Chao-Han Huck Yang, Sabato Marco Siniscalchi, Chin-Hui Lee
We propose using an adversarial autoencoder (AAE) to replace generative adversarial network (GAN) in the private aggregation of teacher ensembles (PATE), a solution for ensuring di…
USTC-NELSLIP System Description for DIHARD-III Challenge
Yuxuan Wang, Maokui He, Shutong Niu +6
This system description describes our submission system to the Third DIHARD Speech Diarization Challenge. Besides the traditional clustering based system, the innovation of our sys…
A Four-Stage Data Augmentation Approach to ResNet-Conformer Based Acoustic Modeling for Sound Event Localization and Detection
Qing Wang, Jun Du, Hua-Xin Wu +3
In this paper, we propose a novel four-stage data augmentation approach to ResNet-Conformer based acoustic modeling for sound event localization and detection (SELD). First, we exp…
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…
Convolutional-Recurrent Neural Networks for Speech Enhancement
Han Zhao, Shuayb Zarar, Ivan Tashev +1
We propose an end-to-end model based on convolutional and recurrent neural networks for speech enhancement. Our model is purely data-driven and does not make any assumptions about…
Multi-Objective Learning and Mask-Based Post-Processing for Deep Neural Network Based Speech Enhancement
Yong Xu, Jun Du, Zhen Huang +2
We propose a multi-objective framework to learn both secondary targets not directly related to the intended task of speech enhancement (SE) and the primary target of the clean log-…