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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; Li, Haizhou. - : arXiv, 2020 Abstract: Emotional voice conversion aims to convert the emotion of speech from one state to another while preserving the linguistic content and speaker identity. The prior studies on emotional voice conversion are mostly carried out under the assumption that emotion is speaker-dependent. We consider that there is a common code between speakers for emotional expression in a spoken language, therefore, a speaker-independent mapping between emotional states is possible. In this paper, we propose a speaker-independent emotional voice conversion framework, that can convert anyone's emotion without the need for parallel data. We propose a VAW-GAN based encoder-decoder structure to learn the spectrum and prosody mapping. We perform prosody conversion by using continuous wavelet transform (CWT) to model the temporal dependencies. We also investigate the use of F0 as an additional input to the decoder to improve emotion conversion performance. Experiments show that the proposed speaker-independent framework achieves ... : Accepted by Interspeech 2020 ... Keyword: Artificial Intelligence cs.AI; 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/2005.07025 https://dx.doi.org/10.48550/arxiv.2005.07025 BASE Hide 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. - : arXiv, 2020 BASE Show details

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