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1
Deep generative factorization for speech signal ...
Abstract: Various information factors are blended in speech signals, which forms the primary difficulty for most speech information processing tasks. An intuitive idea is to factorize speech signal into individual information factors (e.g., phonetic content and speaker trait), though it turns out to be highly challenging. This paper presents a speech factorization approach based on a novel factorial discriminative normalization flow model (factorial DNF). Experiments conducted on a two-factor case that involves phonetic content and speaker trait demonstrates that the proposed factorial DNF has powerful capability to factorize speech signals and outperforms several comparative models in terms of information representation and manipulation. ... : Submitted to ICASSP 2021 ...
Keyword: Audio and Speech Processing eess.AS; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG; Sound cs.SD
URL: https://dx.doi.org/10.48550/arxiv.2010.14242
https://arxiv.org/abs/2010.14242
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2
On Investigation of Unsupervised Speech Factorization Based on Normalization Flow ...
Sun, Haoran; Cai, Yunqi; Li, Lantian. - : arXiv, 2019
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3
Phonetic-attention scoring for deep speaker features in speaker verification ...
Li, Lantian; Tang, Zhiyuan; Shi, Ying. - : arXiv, 2018
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4
Phonetic Temporal Neural Model for Language Identification ...
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5
Phone-aware Neural Language Identification ...
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6
AP16-OL7: A Multilingual Database for Oriental Languages and A Language Recognition Baseline ...
Wang, Dong; Li, Lantian; Tang, Difei. - : arXiv, 2016
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7
Multi-task Recurrent Model for True Multilingual Speech Recognition ...
Tang, Zhiyuan; Li, Lantian; Wang, Dong. - : arXiv, 2016
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8
System Combination for Short Utterance Speaker Recognition ...
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