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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 – 8 of 8 1 Deep generative factorization for speech signal ... Sun, Haoran; Li, Lantian; Cai, Yunqi. - : arXiv, 2020 BASE Show details 2 On Investigation of Unsupervised Speech Factorization Based on Normalization Flow ... Sun, Haoran; Cai, Yunqi; Li, Lantian. - : arXiv, 2019 BASE Show details 3 Phonetic-attention scoring for deep speaker features in speaker verification ... Li, Lantian; Tang, Zhiyuan; Shi, Ying. - : arXiv, 2018 BASE Show details 4 Phonetic Temporal Neural Model for Language Identification ... Tang, Zhiyuan; Wang, Dong; Chen, Yixiang. - : arXiv, 2017 BASE Show details 5 Phone-aware Neural Language Identification ... Tang, Zhiyuan; Wang, Dong; Chen, Yixiang. - : arXiv, 2017 BASE Show details 6 AP16-OL7: A Multilingual Database for Oriental Languages and A Language Recognition Baseline ... Wang, Dong; Li, Lantian; Tang, Difei. - : arXiv, 2016 BASE Show details 7 Multi-task Recurrent Model for True Multilingual Speech Recognition ... Tang, Zhiyuan; Li, Lantian; Wang, Dong. - : arXiv, 2016 BASE Show details 8 System Combination for Short Utterance Speaker Recognition ... Li, Lantian; Wang, Dong; Zhang, Xiaodong; Zheng, Thomas Fang; Jin, Panshi. - : arXiv, 2016 Abstract: For text-independent short-utterance speaker recognition (SUSR), the performance often degrades dramatically. This paper presents a combination approach to the SUSR tasks with two phonetic-aware systems: one is the DNN-based i-vector system and the other is our recently proposed subregion-based GMM-UBM system. The former employs phone posteriors to construct an i-vector model in which the shared statistics offers stronger robustness against limited test data, while the latter establishes a phone-dependent GMM-UBM system which represents speaker characteristics with more details. A score-level fusion is implemented to integrate the respective advantages from the two systems. Experimental results show that for the text-independent SUSR task, both the DNN-based i-vector system and the subregion-based GMM-UBM system outperform their respective baselines, and the score-level system combination delivers performance improvement. ... : APSIPA ASC 2016 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Neural and Evolutionary Computing cs.NE URL: https://arxiv.org/abs/1603.09460 https://dx.doi.org/10.48550/arxiv.1603.09460 BASE Hide details

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