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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 – 7 of 7 1 Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge ... Ruan, Yu-Ping; Zhu, Xiaodan; Ling, Zhen-Hua. - : arXiv, 2019 BASE Show details 2 Spelling Error Correction Using a Nested RNN Model and Pseudo Training Data ... Li, Hao; Wang, Yang; Liu, Xinyu. - : arXiv, 2018 BASE Show details 3 Commonsense Knowledge Enhanced Embeddings for Solving Pronoun Disambiguation Problems in Winograd Schema Challenge ... Liu, Quan; Jiang, Hui; Ling, Zhen-Hua. - : arXiv, 2016 BASE Show details 4 Deep Bottleneck Features for Spoken Language Identification Jiang, Bing; Song, Yan; Wei, Si. - : Public Library of Science, 2014 BASE Show details 5 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 6 A new method for mispronunciation detection using support vector machine based on pronunciation space models Wei, Si; Wang, Ren-Hua; Hu, Guoping... In: Speech communication. - Amsterdam [u.a.] : Elsevier 51 (2009) 10, 896-905 BLLDB OLC Linguistik Show details 7 HETERONYM VERIFICATION FOR MANDARIN SPEECH SYNTHESIS Lu Heng; Ling Zhenhua; Wei Si... In: http://isca-speech.org/archive_open/archive_papers/iscslp2008/137.pdf BASE Show details

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