Zobrazeno 1 - 10 of 28 pro vyhledávání: '"Yu Ting Yeung"'
4
CorrectSpeech: A Fully Automated System for Speech Correction and Accent Reduction
Autor: Daxin Tan, Liqun Deng, Nianzu Zheng, Yu Ting Yeung, Xin Jiang, Xiao Chen, Tan Lee
This study propose a fully automated system for speech correction and accent reduction. Consider the application scenario that a recorded speech audio contains certain errors, e.g., inappropriate words, mispronunciations, that need to be corrected. T
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55fe48cd42469df24ebd2ed1ff932b55
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6
Fcl-Taco2: Towards Fast, Controllable and Lightweight Text-to-Speech Synthesis
Autor: Yu Ting Yeung, Nianzu Zheng, Disong Wang, Helen Meng, Liqun Deng, Xunying Liu, Yang Zhang, Xiao Chen
Publikováno v: ICASSP
Sequence-to-sequence (seq2seq) learning has greatly improved text-to-speech (TTS) synthesis performance, but effective implementation on resource-restricted devices remains challenging as seq2seq models are usually computationally expensive and memor
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::6fca0deb38e16d2d863988edf3989c68
https://doi.org/10.1109/icassp39728.2021.9414870
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7
Unsupervised Domain Adaptation for Dysarthric Speech Detection via Domain Adversarial Training and Mutual Information Minimization
Autor: Xiao Chen, Liqun Deng, Xunying Liu, Yu Ting Yeung, Helen Meng, Disong Wang
Dysarthric speech detection (DSD) systems aim to detect characteristics of the neuromotor disorder from speech. Such systems are particularly susceptible to domain mismatch where the training and testing data come from the source and target domains r
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c4c1f9fd40331e1f0ac2c2f21e3944ed
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8
VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion
Autor: Xunying Liu, Yu Ting Yeung, Helen Meng, Xiao Chen, Liqun Deng, Disong Wang
One-shot voice conversion (VC), which performs conversion across arbitrary speakers with only a single target-speaker utterance for reference, can be effectively achieved by speech representation disentanglement. Existing work generally ignores the c
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a0f25900987f706d0b515bf54af00d1
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9
CoCA-MDD: A Coupled Cross-Attention based Framework for Streaming Mispronunciation Detection and Diagnosis
Autor: Nianzu Zheng, Liqun Deng, Wenyong Huang, Yu Ting Yeung, Baohua Xu, Yuanyuan Guo, Yasheng Wang, Xiao Chen, Xin Jiang, Qun Liu
Mispronunciation detection and diagnosis (MDD) is a popular research focus in computer-aided pronunciation training (CAPT) systems. End-to-end (e2e) approaches are becoming dominant in MDD. However an e2e MDD model usually requires entire speech utte
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9ae6803b10e9d4bcda4209575a1448bd
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10
Conv-Transformer Transducer: Low Latency, Low Frame Rate, Streamable End-to-End Speech Recognition
Autor: Xiao Chen, Yu Ting Yeung, Wenchao Hu, Wenyong Huang
Publikováno v: INTERSPEECH
Transformer has achieved competitive performance against state-of-the-art end-to-end models in automatic speech recognition (ASR), and requires significantly less training time than RNN-based models. The original Transformer, with encoder-decoder arc
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a5c52f16f3135a4f89fa72375f4d97b
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