28 citations · 109 across the 36 of their papers we have counts for
10 papers · 1 filter
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…