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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. - : 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; Liu, Jun-Hua; McLoughlin, Ian Vince; Dai, Li-Rong. - : Public Library of Science, 2014 Abstract: A key problem in spoken language identification (LID) is to design effective representations which are specific to language information. For example, in recent years, representations based on both phonotactic and acoustic features have proven their effectiveness for LID. Although advances in machine learning have led to significant improvements, LID performance is still lacking, especially for short duration speech utterances. With the hypothesis that language information is weak and represented only latently in speech, and is largely dependent on the statistical properties of the speech content, existing representations may be insufficient. Furthermore they may be susceptible to the variations caused by different speakers, specific content of the speech segments, and background noise. To address this, we propose using Deep Bottleneck Features (DBF) for spoken LID, motivated by the success of Deep Neural Networks (DNN) in speech recognition. We show that DBFs can form a low-dimensional compact representation of the original inputs with a powerful descriptive and discriminative capability. To evaluate the effectiveness of this, we design two acoustic models, termed DBF-TV and parallel DBF-TV (PDBF-TV), using a DBF based i-vector representation for each speech utterance. Results on NIST language recognition evaluation 2009 (LRE09) show significant improvements over state-of-the-art systems. By fusing the output of phonotactic and acoustic approaches, we achieve an EER of 1.08%, 1.89% and 7.01% for 30 s, 10 s and 3 s test utterances respectively. Furthermore, various DBF configurations have been extensively evaluated, and an optimal system proposed. Keyword: T Technology URL: https://doi.org/10.1371/journal.pone.0100795 https://kar.kent.ac.uk/48803/ BASE Hide 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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