8 citations · 21 across the 13 of their papers we have counts for
13 papers
ImGeoNet: Image-induced Geometry-aware Voxel Representation for Multi-view 3D Object Detection
Tao Tu, Shun-Po Chuang, Yu-Lun Liu +5
We propose ImGeoNet, a multi-view image-based 3D object detection framework that models a 3D space by an image-induced geometry-aware voxel representation. Unlike previous methods…
EURO: ESPnet Unsupervised ASR Open-source Toolkit
Dongji Gao, Jiatong Shi, Shun-Po Chuang +4
This paper describes the ESPnet Unsupervised ASR Open-source Toolkit (EURO), an end-to-end open-source toolkit for unsupervised automatic speech recognition (UASR). EURO adopts the…
Learning Phone Recognition from Unpaired Audio and Phone Sequences Based on Generative Adversarial Network
Da-rong Liu, Po-chun Hsu, Yi-chen Chen +4
ASR has been shown to achieve great performance recently. However, most of them rely on massive paired data, which is not feasible for low-resource languages worldwide. This paper…
Anticipation-Free Training for Simultaneous Machine Translation
Chih-Chiang Chang, Shun-Po Chuang, Hung-yi Lee
Simultaneous machine translation (SimulMT) speeds up the translation process by starting to translate before the source sentence is completely available. It is difficult due to lim…
Investigating the Reordering Capability in CTC-based Non-Autoregressive End-to-End Speech Translation
Shun-Po Chuang, Yung-Sung Chuang, Chih-Chiang Chang +1
We study the possibilities of building a non-autoregressive speech-to-text translation model using connectionist temporal classification (CTC), and use CTC-based automatic speech r…
Non-autoregressive Mandarin-English Code-switching Speech Recognition
Shun-Po Chuang, Heng-Jui Chang, Sung-Feng Huang +1
Mandarin-English code-switching (CS) is frequently used among East and Southeast Asian people. However, the intra-sentence language switching of the two very different languages ma…
Stabilizing Label Assignment for Speech Separation by Self-supervised Pre-training
Sung-Feng Huang, Shun-Po Chuang, Da-Rong Liu +3
Speech separation has been well developed, with the very successful permutation invariant training (PIT) approach, although the frequent label assignment switching happening during…
Worse WER, but Better BLEU? Leveraging Word Embedding as Intermediate in Multitask End-to-End Speech Translation
Shun-Po Chuang, Tzu-Wei Sung, Alexander H. Liu +1
Speech translation (ST) aims to learn transformations from speech in the source language to the text in the target language. Previous works show that multitask learning improves th…
Training a code-switching language model with monolingual data
Shun-Po Chuang, Tzu-Wei Sung, Hung-Yi Lee
A lack of code-switching data complicates the training of code-switching (CS) language models. We propose an approach to train such CS language models on monolingual data only. By…
Sequence-to-sequence Automatic Speech Recognition with Word Embedding Regularization and Fused Decoding
Alexander H. Liu, Tzu-Wei Sung, Shun-Po Chuang +2
In this paper, we investigate the benefit that off-the-shelf word embedding can bring to the sequence-to-sequence (seq-to-seq) automatic speech recognition (ASR). We first introduc…
Code-switching Sentence Generation by Generative Adversarial Networks and its Application to Data Augmentation
Ching-Ting Chang, Shun-Po Chuang, Hung-Yi Lee
Code-switching is about dealing with alternative languages in speech or text. It is partially speaker-depend and domain-related, so completely explaining the phenomenon by linguist…
Towards Audio to Scene Image Synthesis using Generative Adversarial Network
Chia-Hung Wan, Shun-Po Chuang, Hung-Yi Lee
Humans can imagine a scene from a sound. We want machines to do so by using conditional generative adversarial networks (GANs). By applying the techniques including spectral norm,…
Attention-based Memory Selection Recurrent Network for Language Modeling
Da-Rong Liu, Shun-Po Chuang, Hung-yi Lee
Recurrent neural networks (RNNs) have achieved great success in language modeling. However, since the RNNs have fixed size of memory, their memory cannot store all the information…