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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 17 of 17 1 A Complementary Joint Training Approach Using Unpaired Speech and Text for Low-Resource Automatic Speech Recognition ... Du, Ye-Qian; Zhang, Jie; Zhu, Qiu-Shi. - : arXiv, 2022 BASE Show details 2 XLST: Cross-lingual Self-training to Learn Multilingual Representation for Low Resource Speech Recognition ... Zhang, Zi-Qiang; Song, Yan; Wu, Ming-Hui. - : arXiv, 2021 BASE Show details 3 Recognition-Synthesis Based Non-Parallel Voice Conversion with Adversarial Learning ... Zhang, Jing-Xuan; Ling, Zhen-Hua; Dai, Li-Rong. - : arXiv, 2020 BASE Show details 4 Improving Sequence-to-Sequence Acoustic Modeling by Adding Text-Supervision ... Zhang, Jing-Xuan; Ling, Zhen-Hua; Jiang, Yuan; Liu, Li-Juan; Liang, Chen; Dai, Li-Rong. - : arXiv, 2018 Abstract: This paper presents methods of making using of text supervision to improve the performance of sequence-to-sequence (seq2seq) voice conversion. Compared with conventional frame-to-frame voice conversion approaches, the seq2seq acoustic modeling method proposed in our previous work achieved higher naturalness and similarity. In this paper, we further improve its performance by utilizing the text transcriptions of parallel training data. First, a multi-task learning structure is designed which adds auxiliary classifiers to the middle layers of the seq2seq model and predicts linguistic labels as a secondary task. Second, a data-augmentation method is proposed which utilizes text alignment to produce extra parallel sequences for model training. Experiments are conducted to evaluate our proposed method with training sets at different sizes. Experimental results show that the multi-task learning with linguistic labels is effective at reducing the errors of seq2seq voice conversion. The data-augmentation method can ... : 5 pages, 4 figures, 2 tables. Submitted to IEEE ICASSP 2019 ... Keyword: Audio and Speech Processing eess.AS; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Sound cs.SD URL: https://arxiv.org/abs/1811.08111 https://dx.doi.org/10.48550/arxiv.1811.08111 BASE Hide details 5 LID-senones and their statistics for language identification Jin, Ma; Song, Yan; McLoughlin, Ian Vince. - : Institute of Electrical and Electronics Engineers, 2017 BASE Show details 6 A human neurodevelopmental model for Williams syndrome. Chailangkarn, Thanathom; Trujillo, Cleber A; Freitas, Beatriz C... In: Nature, vol 536, iss 7616 (2016) BASE Show details 7 A human neurodevelopmental model for Williams syndrome. Chailangkarn, Thanathom; Trujillo, Cleber A; Freitas, Beatriz C... In: Nature, vol 536, iss 7616 (2016) BASE Show details 8 Improvements on Deep Bottleneck Network based I-Vector Representation for Spoken Language Identification Song, Yan; Cui, Ruilian; McLoughlin, Ian Vince. - 2016 BASE Show details 9 Deep Bottleneck Feature for Image Classification Song, Yan; McLoughlin, Ian Vince; Dai, Li-Rong. - 2015 BASE Show details 10 HMM-based unit selection speech synthesis using log likelihood ratios derived from perceptual data Xia, Xian-Jun; Ling, Zhen-Hua; Jiang, Yuan... In: Speech communication. - Amsterdam [u.a.] : Elsevier 63 (2014), 27-37 OLC Linguistik Show details 11 Deep Bottleneck Features for Spoken Language Identification Jiang, Bing; Song, Yan; Wei, Si. - : Public Library of Science, 2014 BASE Show details 12 Whisper-to-speech conversion using restricted Boltzmann machine arrays Li, Jing-jie; McLoughlin, Ian Vince; Dai, Li-Rong. - : IET Digital Library, 2014 BASE Show details 13 Deep bottleneck features for spoken language identification Jiang, Bing; Song, Yan; Wei, Si. - : Public Library of Science, 2014 BASE Show details 14 Minimum Kullback-Leibler divergence parameter generation for HMM-based speech synthesis Ling, Zhen-Hua; Dai, Li-Rong In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 20 (2012) 5, 1492-1502 BLLDB OLC Linguistik Show details 15 Trust region-based optimization for maximum mutual information estimation of HMMs in speech recognition Hu, Yu; Jiang, Hui; Liu, Cong... In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 19 (2011) 8, 2474-2485 BLLDB OLC Linguistik Show details 16 Intelligence in Williams Syndrome is related to STX1A, which encodes a component of the presynaptic SNARE complex. Gao, Michael C; Bellugi, Ursula; Dai, Li... In: PloS one, vol 5, iss 4 (2010) BASE Show details 17 Intelligence in Williams Syndrome Is Related to STX1A, Which Encodes a Component of the Presynaptic SNARE Complex Gao, Michael C.; Bellugi, Ursula; Dai, Li. - : Public Library of Science, 2010 BASE Show details

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