28 citations · 109 across the 36 of their papers we have counts for
38 papers
DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question Answering
Guan-Ting Lin, Yung-Sung Chuang, Ho-Lam Chung +7
Spoken Question Answering (SQA) is to find the answer from a spoken document given a question, which is crucial for personal assistants when replying to the queries from the users.…
Towards Lifelong Learning of End-to-end ASR
Heng-Jui Chang, Hung-yi Lee, Lin-shan Lee
Automatic speech recognition (ASR) technologies today are primarily optimized for given datasets; thus, any changes in the application environment (e.g., acoustic conditions or top…
FragmentVC: Any-to-Any Voice Conversion by End-to-End Extracting and Fusing Fine-Grained Voice Fragments With Attention
Yist Y. Lin, Chung-Ming Chien, Jheng-Hao Lin +2
Any-to-any voice conversion aims to convert the voice from and to any speakers even unseen during training, which is much more challenging compared to one-to-one or many-to-many ta…
Defending Your Voice: Adversarial Attack on Voice Conversion
Chien-yu Huang, Yist Y. Lin, Hung-yi Lee +1
Substantial improvements have been achieved in recent years in voice conversion, which converts the speaker characteristics of an utterance into those of another speaker without ch…
End-to-end Whispered Speech Recognition with Frequency-weighted Approaches and Pseudo Whisper Pre-training
Heng-Jui Chang, Alexander H. Liu, Hung-yi Lee +1
Whispering is an important mode of human speech, but no end-to-end recognition results for it were reported yet, probably due to the scarcity of available whispered speech data. In…
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…
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…
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…
Completely Unsupervised Speech Recognition By A Generative Adversarial Network Harmonized With Iteratively Refined Hidden Markov Models
Kuan-Yu Chen, Che-Ping Tsai, Da-Rong Liu +2
Producing a large annotated speech corpus for training ASR systems remains difficult for more than 95% of languages all over the world which are low-resourced, but collecting a rel…
Improved Audio Embeddings by Adjacency-Based Clustering with Applications in Spoken Term Detection
Sung-Feng Huang, Yi-Chen Chen, Hung-yi Lee +1
Embedding audio signal segments into vectors with fixed dimensionality is attractive because all following processing will be easier and more efficient, for example modeling, class…
Adversarial Training of End-to-end Speech Recognition Using a Criticizing Language Model
Alexander H. Liu, Hung-yi Lee, Lin-shan Lee
In this paper we proposed a novel Adversarial Training (AT) approach for end-to-end speech recognition using a Criticizing Language Model (CLM). In this way the CLM and the automat…
Almost-unsupervised Speech Recognition with Close-to-zero Resource Based on Phonetic Structures Learned from Very Small Unpaired Speech and Text Data
Yi-Chen Chen, Chia-Hao Shen, Sung-Feng Huang +2
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…
Rhythm-Flexible Voice Conversion without Parallel Data Using Cycle-GAN over Phoneme Posteriorgram Sequences
Cheng-chieh Yeh, Po-chun Hsu, Ju-chieh Chou +2
Speaking rate refers to the average number of phonemes within some unit time, while the rhythmic patterns refer to duration distributions for realizations of different phonemes wit…
Segmental Audio Word2Vec: Representing Utterances as Sequences of Vectors with Applications in Spoken Term Detection
Yu-Hsuan Wang, Hung-yi Lee, Lin-shan Lee
While Word2Vec represents words (in text) as vectors carrying semantic information, audio Word2Vec was shown to be able to represent signal segments of spoken words as vectors carr…
Phonetic-and-Semantic Embedding of Spoken Words with Applications in Spoken Content Retrieval
Yi-Chen Chen, Sung-Feng Huang, Chia-Hao Shen +2
Word embedding or Word2Vec has been successful in offering semantics for text words learned from the context of words. Audio Word2Vec was shown to offer phonetic structures for spo…
Transcribing Lyrics From Commercial Song Audio: The First Step Towards Singing Content Processing
Che-Ping Tsai, Yi-Lin Tuan, Lin-shan Lee
Spoken content processing (such as retrieval and browsing) is maturing, but the singing content is still almost completely left out. Songs are human voice carrying plenty of semant…
Scalable Sentiment for Sequence-to-sequence Chatbot Response with Performance Analysis
Chih-Wei Lee, Yau-Shian Wang, Tsung-Yuan Hsu +3
Conventional seq2seq chatbot models only try to find the sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the outpu…
Multi-target Voice Conversion without Parallel Data by Adversarially Learning Disentangled Audio Representations
Ju-chieh Chou, Cheng-chieh Yeh, Hung-yi Lee +1
Recently, cycle-consistent adversarial network (Cycle-GAN) has been successfully applied to voice conversion to a different speaker without parallel data, although in those approac…
Completely Unsupervised Phoneme Recognition by Adversarially Learning Mapping Relationships from Audio Embeddings
Da-Rong Liu, Kuan-Yu Chen, Hung-Yi Lee +1
Unsupervised discovery of acoustic tokens from audio corpora without annotation and learning vector representations for these tokens have been widely studied. Although these techni…
Unsupervised Discovery of Structured Acoustic Tokens with Applications to Spoken Term Detection
Cheng-Tao Chung, Lin-Shan Lee
In this paper, we compare two paradigms for unsupervised discovery of structured acoustic tokens directly from speech corpora without any human annotation. The Multigranular Paradi…
Personalized word representations Carrying Personalized Semantics Learned from Social Network Posts
Zih-Wei Lin, Tzu-Wei Sung, Hung-Yi Lee +1
Distributed word representations have been shown to be very useful in various natural language processing (NLP) application tasks. These word vectors learned from huge corpora very…
Order-Preserving Abstractive Summarization for Spoken Content Based on Connectionist Temporal Classification
Bo-Ru Lu, Frank Shyu, Yun-Nung Chen +2
Connectionist temporal classification (CTC) is a powerful approach for sequence-to-sequence learning, and has been popularly used in speech recognition. The central ideas of CTC in…