62 citations · 449 across the 49 of their papers we have counts for
51 papers
Conditional Modeling Based Automatic Video Summarization
Jia-Hong Huang, Chao-Han Huck Yang, Pin-Yu Chen +2
The aim of video summarization is to shorten videos automatically while retaining the key information necessary to convey the overall story. Video summarization methods mainly rely…
Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Prompting
Chao-Han Huck Yang, Yile Gu, Yi-Chieh Liu +3
We explore the ability of large language models (LLMs) to act as speech recognition post-processors that perform rescoring and error correction. Our first focus is on instruction p…
Can Whisper perform speech-based in-context learning?
Siyin Wang, Chao-Han Huck Yang, Ji Wu +1
This paper investigates the in-context learning abilities of the Whisper automatic speech recognition (ASR) models released by OpenAI. A novel speech-based in-context learning (SIC…
Causal Video Summarizer for Video Exploration
Jia-Hong Huang, Chao-Han Huck Yang, Pin-Yu Chen +2
Recently, video summarization has been proposed as a method to help video exploration. However, traditional video summarization models only generate a fixed video summary which is…
Causalainer: Causal Explainer for Automatic Video Summarization
Jia-Hong Huang, Chao-Han Huck Yang, Pin-Yu Chen +2
The goal of video summarization is to automatically shorten videos such that it conveys the overall story without losing relevant information. In many application scenarios, improp…
From English to More Languages: Parameter-Efficient Model Reprogramming for Cross-Lingual Speech Recognition
Chao-Han Huck Yang, Bo Li, Yu Zhang +4
In this work, we propose a new parameter-efficient learning framework based on neural model reprogramming for cross-lingual speech recognition, which can \textbf{re-purpose} well-t…
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…
Inference and Denoise: Causal Inference-based Neural Speech Enhancement
Tsun-An Hsieh, Chao-Han Huck Yang, Pin-Yu Chen +2
This study addresses the speech enhancement (SE) task within the causal inference paradigm by modeling the noise presence as an intervention. Based on the potential outcome framewo…
Low-Resource Music Genre Classification with Cross-Modal Neural Model Reprogramming
Yun-Ning Hung, Chao-Han Huck Yang, Pin-Yu Chen +1
Transfer learning (TL) approaches have shown promising results when handling tasks with limited training data. However, considerable memory and computational resources are often re…
Certified Robustness of Quantum Classifiers against Adversarial Examples through Quantum Noise
Jhih-Cing Huang, Yu-Lin Tsai, Chao-Han Huck Yang +4
Recently, quantum classifiers have been found to be vulnerable to adversarial attacks, in which quantum classifiers are deceived by imperceptible noises, leading to misclassificati…
An Ensemble Teacher-Student Learning Approach with Poisson Sub-sampling to Differential Privacy Preserving Speech Recognition
Chao-Han Huck Yang, Jun Qi, Sabato Marco Siniscalchi +1
We propose an ensemble learning framework with Poisson sub-sampling to effectively train a collection of teacher models to issue some differential privacy (DP) guarantee for traini…
Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regression
Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen +1
The noisy intermediate-scale quantum (NISQ) devices enable the implementation of the variational quantum circuit (VQC) for quantum neural networks (QNN). Although the VQC-based QNN…
From 2D Images to 3D Model:Weakly Supervised Multi-View Face Reconstruction with Deep Fusion
Weiguang Zhao, Chaolong Yang, Jianan Ye +6
While weakly supervised multi-view face reconstruction (MVR) is garnering increased attention, one critical issue still remains open: how to effectively interact and fuse multiple…
Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing
Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen +1
This work focuses on designing low complexity hybrid tensor networks by considering trade-offs between the model complexity and practical performance. Firstly, we exploit a low-ran…
Treatment Learning Causal Transformer for Noisy Image Classification
Chao-Han Huck Yang, I-Te Danny Hung, Yi-Chieh Liu +1
Current top-notch deep learning (DL) based vision models are primarily based on exploring and exploiting the inherent correlations between training data samples and their associate…
A study on joint modeling and data augmentation of multi-modalities for audio-visual scene classification
Qing Wang, Jun Du, Siyuan Zheng +8
In this paper, we propose two techniques, namely joint modeling and data augmentation, to improve system performances for audio-visual scene classification (AVSC). We employ pre-tr…
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing
Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen +2
The rapid development of quantum computing has demonstrated many unique characteristics of quantum advantages, such as richer feature representation and more secured protection on…
Mitigating Closed-model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition
Chao-Han Huck Yang, Zeeshan Ahmed, Yile Gu +5
In this work, we aim to enhance the system robustness of end-to-end automatic speech recognition (ASR) against adversarially-noisy speech examples. We focus on a rigorous and empir…
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…
Pessimistic Model Selection for Offline Deep Reinforcement Learning
Chao-Han Huck Yang, Zhengling Qi, Yifan Cui +1
Deep Reinforcement Learning (DRL) has demonstrated great potentials in solving sequential decision making problems in many applications. Despite its promising performance, practica…
QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks
Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen
The advent of noisy intermediate-scale quantum (NISQ) computers raises a crucial challenge to design quantum neural networks for fully quantum learning tasks. To bridge the gap, th…
A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge Transfer
Hu Hu, Sabato Marco Siniscalchi, Chao-Han Huck Yang +1
We propose a variational Bayesian (VB) approach to learning distributions of latent variables in deep neural network (DNN) models for cross-domain knowledge transfer, to address ac…
Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Recognition
Hao Yen, Pin-Jui Ku, Chao-Han Huck Yang +4
In this study, we propose a novel adversarial reprogramming (AR) approach for low-resource spoken command recognition (SCR), and build an AR-SCR system. The AR procedure aims to mo…
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…