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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 – 10 of 10 1 Emotion Intensity and its Control for Emotional Voice Conversion ... Zhou, Kun; Sisman, Berrak; Rana, Rajib. - : arXiv, 2022 BASE Show details 2 Limited Data Emotional Voice Conversion Leveraging Text-to-Speech: Two-stage Sequence-to-Sequence Training ... Zhou, Kun; Sisman, Berrak; Li, Haizhou. - : arXiv, 2021 BASE Show details 3 Identity Conversion for Emotional Speakers: A Study for Disentanglement of Emotion Style and Speaker Identity ... Du, Zongyang; Sisman, Berrak; Zhou, Kun. - : arXiv, 2021 BASE Show details 4 CRSLab: An Open-Source Toolkit for Building Conversational Recommender System ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Cheng, Yuan; Li, Yaliang. - : Underline Science Inc., 2021 BASE Show details 5 Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Zhou, Kun. - : Underline Science Inc., 2021 BASE Show details 6 VAW-GAN for Disentanglement and Recomposition of Emotional Elements in Speech ... Zhou, Kun; Sisman, Berrak; Li, Haizhou. - : arXiv, 2020 BASE Show details 7 Seen and Unseen emotional style transfer for voice conversion with a new emotional speech dataset ... Zhou, Kun; Sisman, Berrak; Liu, Rui. - : arXiv, 2020 BASE Show details 8 Converting Anyone's Emotion: Towards Speaker-Independent Emotional Voice Conversion ... Zhou, Kun; Sisman, Berrak; Zhang, Mingyang. - : arXiv, 2020 BASE Show details 9 Transforming Spectrum and Prosody for Emotional Voice Conversion with Non-Parallel Training Data ... Zhou, Kun; Sisman, Berrak; Li, Haizhou. - : arXiv, 2020 BASE Show details 10 VAW-GAN for Singing Voice Conversion with Non-parallel Training Data ... Lu, Junchen; Zhou, Kun; Sisman, Berrak; Li, Haizhou. - : arXiv, 2020 Abstract: Singing voice conversion aims to convert singer's voice from source to target without changing singing content. Parallel training data is typically required for the training of singing voice conversion system, that is however not practical in real-life applications. Recent encoder-decoder structures, such as variational autoencoding Wasserstein generative adversarial network (VAW-GAN), provide an effective way to learn a mapping through non-parallel training data. In this paper, we propose a singing voice conversion framework that is based on VAW-GAN. We train an encoder to disentangle singer identity and singing prosody (F0 contour) from phonetic content. By conditioning on singer identity and F0, the decoder generates output spectral features with unseen target singer identity, and improves the F0 rendering. Experimental results show that the proposed framework achieves better performance than the baseline frameworks. ... : Accepted to APSIPA ASC 2020 ... Keyword: Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Sound cs.SD URL: https://arxiv.org/abs/2008.03992 https://dx.doi.org/10.48550/arxiv.2008.03992 BASE Hide details

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