371 citations · 2.1k across the 193 of their papers we have counts for
27 papers · 1 filter
MITAS: A Compressed Time-Domain Audio Separation Network with Parameter Sharing
Chao-I Tuan, Yuan-Kuei Wu, Hung-yi Lee +1
Deep learning methods have brought substantial advancements in speech separation (SS). Nevertheless, it remains challenging to deploy deep-learning-based models on edge devices. Th…
Towards Robust Neural Vocoding for Speech Generation: A Survey
Po-chun Hsu, Chun-hsuan Wang, Andy T. Liu +1
Recently, neural vocoders have been widely used in speech synthesis tasks, including text-to-speech and voice conversion. However, when encountering data distribution mismatch betw…
J-Net: Randomly weighted U-Net for audio source separation
Bo-Wen Chen, Yen-Min Hsu, Hung-Yi Lee
Several results in the computer vision literature have shown the potential of randomly weighted neural networks. While they perform fairly well as feature extractors for discrimina…
Deep Long Audio Inpainting
Ya-Liang Chang, Kuan-Ying Lee, Po-Yu Wu +2
Long (> 200 ms) audio inpainting, to recover a long missing part in an audio segment, could be widely applied to audio editing tasks and transmission loss recovery. It is a very ch…
What does a network layer hear? Analyzing hidden representations of end-to-end ASR through speech synthesis
Chung-Yi Li, Pei-Chieh Yuan, Hung-Yi Lee
End-to-end speech recognition systems have achieved competitive results compared to traditional systems. However, the complex transformations involved between layers given highly v…
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…
Tree Transformer: Integrating Tree Structures into Self-Attention
Yau-Shian Wang, Hung-Yi Lee, Yun-Nung Chen
Pre-training Transformer from large-scale raw texts and fine-tuning on the desired task have achieved state-of-the-art results on diverse NLP tasks. However, it is unclear what the…
Interrupted and cascaded permutation invariant training for speech separation
Gene-Ping Yang, Szu-Lin Wu, Yao-Wen Mao +2
Permutation Invariant Training (PIT) has long been a stepping stone method for training speech separation model in handling the label ambiguity problem. With PIT selecting the mini…
Meta Learning for End-to-End Low-Resource Speech Recognition
Jui-Yang Hsu, Yuan-Jui Chen, Hung-yi Lee
In this paper, we proposed to apply meta learning approach for low-resource automatic speech recognition (ASR). We formulated ASR for different languages as different tasks, and me…
Adversarial Attacks on Spoofing Countermeasures of automatic speaker verification
Songxiang Liu, Haibin Wu, Hung-yi Lee +1
High-performance spoofing countermeasure systems for automatic speaker verification (ASV) have been proposed in the ASVspoof 2019 challenge. However, the robustness of such systems…
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…
Towards Unsupervised Speech Recognition and Synthesis with Quantized Speech Representation Learning
Alexander H. Liu, Tao Tu, Hung-yi Lee +1
In this paper we propose a Sequential Representation Quantization AutoEncoder (SeqRQ-AE) to learn from primarily unpaired audio data and produce sequences of representations very c…
Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders
Andy T. Liu, Shu-wen Yang, Po-Han Chi +2
We present Mockingjay as a new speech representation learning approach, where bidirectional Transformer encoders are pre-trained on a large amount of unlabeled speech. Previous spe…
SpeechBERT: An Audio-and-text Jointly Learned Language Model for End-to-end Spoken Question Answering
Yung-Sung Chuang, Chi-Liang Liu, Hung-Yi Lee +1
While various end-to-end models for spoken language understanding tasks have been explored recently, this paper is probably the first known attempt to challenge the very difficult…
DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs
Yi-Lin Tuan, Yun-Nung Chen, Hung-yi Lee
Data-driven, knowledge-grounded neural conversation models are capable of generating more informative responses. However, these models have not yet demonstrated that they can zero-…
Zero-shot Reading Comprehension by Cross-lingual Transfer Learning with Multi-lingual Language Representation Model
Tsung-yuan Hsu, Chi-liang Liu, Hung-yi Lee
Because it is not feasible to collect training data for every language, there is a growing interest in cross-lingual transfer learning. In this paper, we systematically explore zer…
Order-free Learning Alleviating Exposure Bias in Multi-label Classification
Che-Ping Tsai, Hung-Yi Lee
Multi-label classification (MLC) assigns multiple labels to each sample. Prior studies show that MLC can be transformed to a sequence prediction problem with a recurrent neural net…
LAMOL: LAnguage MOdeling for Lifelong Language Learning
Fan-Keng Sun, Cheng-Hao Ho, Hung-Yi Lee
Most research on lifelong learning applies to images or games, but not language. We present LAMOL, a simple yet effective method for lifelong language learning (LLL) based on langu…
Polly Want a Cracker: Analyzing Performance of Parroting on Paraphrase Generation Datasets
Hongren Mao, Hung-yi Lee
Paraphrase generation is an interesting and challenging NLP task which has numerous practical applications. In this paper, we analyze datasets commonly used for paraphrase generati…
Cross-Lingual Transfer Learning for Question Answering
Chia-Hsuan Lee, Hung-Yi Lee
Deep learning based question answering (QA) on English documents has achieved success because there is a large amount of English training examples. However, for most languages, tra…
Unsupervised End-to-End Learning of Discrete Linguistic Units for Voice Conversion
Andy T. Liu, Po-chun Hsu, Hung-yi Lee
We present an unsupervised end-to-end training scheme where we discover discrete subword units from speech without using any labels. The discrete subword units are learned under an…
Mitigating the Impact of Speech Recognition Errors on Spoken Question Answering by Adversarial Domain Adaptation
Chia-Hsuan Lee, Yun-Nung Chen, Hung-Yi Lee
Spoken question answering (SQA) is challenging due to complex reasoning on top of the spoken documents. The recent studies have also shown the catastrophic impact of automatic spee…
Improved Speech Separation with Time-and-Frequency Cross-domain Joint Embedding and Clustering
Gene-Ping Yang, Chao-I Tuan, Hung-Yi Lee +1
Speech separation has been very successful with deep learning techniques. Substantial effort has been reported based on approaches over spectrogram, which is well known as the stan…
From Semi-supervised to Almost-unsupervised Speech Recognition with Very-low Resource by Jointly Learning Phonetic Structures from Audio and Text Embeddings
Yi-Chen Chen, Sung-Feng Huang, Hung-yi Lee +1
Producing a large amount of annotated speech data for training ASR systems remains difficult for more than 95% of languages all over the world which are low-resourced. However, we…