12 citations · 25 across the 11 of their papers we have counts for
11 papers
Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models
Hsuan Su, Ting-Yao Hu, Hema Swetha Koppula +5
While Automatic Speech Recognition (ASR) systems are widely used in many real-world applications, they often do not generalize well to new domains and need to be finetuned on data…
Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesis
Karren Yang, Ting-Yao Hu, Jen-Hao Rick Chang +2
Adapting generic speech recognition models to specific individuals is a challenging problem due to the scarcity of personalized data. Recent works have proposed boosting the amount…
I see what you hear: a vision-inspired method to localize words
Mohammad Samragh, Arnav Kundu, Ting-Yao Hu +5
This paper explores the possibility of using visual object detection techniques for word localization in speech data. Object detection has been thoroughly studied in the contempora…
Synt++: Utilizing Imperfect Synthetic Data to Improve Speech Recognition
Ting-Yao Hu, Mohammadreza Armandpour, Ashish Shrivastava +3
With recent advances in speech synthesis, synthetic data is becoming a viable alternative to real data for training speech recognition models. However, machine learning with synthe…
Subspace Representation Learning for Few-shot Image Classification
Ting-Yao Hu, Zhi-Qi Cheng, Alexander G. Hauptmann
In this paper, we propose a subspace representation learning (SRL) framework to tackle few-shot image classification tasks. It exploits a subspace in local CNN feature space to rep…
Pose Guided Person Image Generation with Hidden p-Norm Regression
Ting-Yao Hu, Alexander G. Hauptmann
In this paper, we propose a novel approach to solve the pose guided person image generation task. We assume that the relation between pose and appearance information can be describ…
SapAugment: Learning A Sample Adaptive Policy for Data Augmentation
Ting-Yao Hu, Ashish Shrivastava, Jen-Hao Rick Chang +5
Data augmentation methods usually apply the same augmentation (or a mix of them) to all the training samples. For example, to perturb data with noise, the noise is sampled from a N…
Project RISE: Recognizing Industrial Smoke Emissions
Yen-Chia Hsu, Ting-Hao 'Kenneth' Huang, Ting-Yao Hu +7
Industrial smoke emissions pose a significant concern to human health. Prior works have shown that using Computer Vision (CV) techniques to identify smoke as visual evidence can in…
Unsupervised Style and Content Separation by Minimizing Mutual Information for Speech Synthesis
Ting-Yao Hu, Ashish Shrivastava, Oncel Tuzel +1
We present a method to generate speech from input text and a style vector that is extracted from a reference speech signal in an unsupervised manner, i.e., no style annotation, suc…
Multi-shot Person Re-identification through Set Distance with Visual Distributional Representation
Ting-Yao Hu, Xiaojun Chang, Alexander G. Hauptmann
Person re-identification aims to identify a specific person at distinct times and locations. It is challenging because of occlusion, illumination, and viewpoint change in camera vi…
Complex spectrogram enhancement by convolutional neural network with multi-metrics learning
Szu-Wei Fu, Ting-yao Hu, Yu Tsao +1
This paper aims to address two issues existing in the current speech enhancement methods: 1) the difficulty of phase estimations; 2) a single objective function cannot consider mul…