Journal ArticleDOI
An overview of voice conversion systems
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TLDR
An overview of real-world applications of VC systems, extensively study existing systems proposed in the literature, and discuss remaining challenges are provided.About:
This article is published in Speech Communication.The article was published on 2017-04-01. It has received 232 citations till now. The article focuses on the topics: Voice analysis & Voice activity detection.read more
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Proceedings ArticleDOI
The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods
Jaime Lorenzo-Trueba,Junichi Yamagishi,Tomoki Toda,Daisuke Saito,Fernando Villavicencio,Tomi Kinnunen,Zhen-Hua Ling +6 more
TL;DR: A brief summary of the state-of-the-art techniques for VC is presented, followed by a detailed explanation of the challenge tasks and the results that were obtained.
Journal ArticleDOI
An Overview of Voice Conversion and Its Challenges: From Statistical Modeling to Deep Learning
TL;DR: This article provides a comprehensive overview of the state-of-the-art of voice conversion techniques and their performance evaluation methods from the statistical approaches to deep learning, and discusses their promise and limitations.
Proceedings ArticleDOI
L2-ARCTIC: A Non-Native English Speech Corpus
Guanlong Zhao,Sinem Sonsaat,Alif Silpachai,Ivana Lucic,Evgeny Chukharev-Hudilainen,John M. Levis,Ricardo Gutierrez-Osuna +6 more
TL;DR: L2-ARCTIC is introduced, a speech corpus of non-native English that is intended for research in voice conversion, accent conversion, and mispronunciation detection, and is publicly accessible at https://psi.tamu.edu/l2-arctic-corpus/.
Journal ArticleDOI
Non-Parallel Sequence-to-Sequence Voice Conversion With Disentangled Linguistic and Speaker Representations
TL;DR: In this paper, a sequence-to-sequence (seq2seq) voice conversion method using non-parallel training data is proposed, which preserves the linguistic representations of source utterances while replacing the speaker representations with the target ones.
Posted Content
High-quality nonparallel voice conversion based on cycle-consistent adversarial network
TL;DR: In this article, a cycle-consistent adversarial network (CycleGAN) was proposed for non-parallel data-based voice conversion using unpaired image-to-image translation.
References
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Deep Sparse Rectifier Neural Networks
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