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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. - : arXiv, 2018 BASE Show details 5 LID-senones and their statistics for language identification Jin, Ma; Song, Yan; McLoughlin, Ian Vince; Dai, Li-Rong. - : Institute of Electrical and Electronics Engineers, 2017 Abstract: Recent research on end-to-end training structures for language identification has raised the possibility that intermediate language-sensitive feature units exist which are analogous to phonetically-sensitive senones in automatic speech recognition systems. Termed LID (language identification)-senones, the statistics derived from these feature units have been shown to be beneficial in discriminating between languages, particularly for short utterances. This paper examines the evidence for the existence of LID-senones before designing and evaluating LID systems based on low and high level statistics of LID-senones with both generative and discriminative models. For the standard NIST LRE 2009 task on 23 languages, LID-senone based systems are shown to outperform state-of-the art DNN/i-vector methods both when LID-senones are used directly for classification and when LID-senone statistics are used for i-vector formation. Keyword: T Technology URL: https://doi.org/10.1109/TASLP.2017.2766023 https://kar.kent.ac.uk/64034/7/08080255.pdf https://kar.kent.ac.uk/64034/ https://kar.kent.ac.uk/64034/1/LID_senone_J_v9.pdf BASE Hide 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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