{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/parallel-data-free-voice-conversion-using","title":"Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks","arxiv_id":"1711.11293","date":"2017-11-30","proceeding":null,"authors":["Takuhiro Kaneko","Hirokazu Kameoka"],"abstract":"We propose a parallel-data-free voice-conversion (VC) method that can learn a\nmapping from source to target speech without relying on parallel data. The\nproposed method is general purpose, high quality, and parallel-data free and\nworks without any extra data, modules, or alignment procedure. It also avoids\nover-smoothing, which occurs in many conventional statistical model-based VC\nmethods. Our method, called CycleGAN-VC, uses a cycle-consistent adversarial\nnetwork (CycleGAN) with gated convolutional neural networks (CNNs) and an\nidentity-mapping loss. A CycleGAN learns forward and inverse mappings\nsimultaneously using adversarial and cycle-consistency losses. This makes it\npossible to find an optimal pseudo pair from unpaired data. Furthermore, the\nadversarial loss contributes to reducing over-smoothing of the converted\nfeature sequence. We configure a CycleGAN with gated CNNs and train it with an\nidentity-mapping loss. This allows the mapping function to capture sequential\nand hierarchical structures while preserving linguistic information. We\nevaluated our method on a parallel-data-free VC task. An objective evaluation\nshowed that the converted feature sequence was near natural in terms of global\nvariance and modulation spectra. A subjective evaluation showed that the\nquality of the converted speech was comparable to that obtained with a Gaussian\nmixture model-based method under advantageous conditions with parallel and\ntwice the amount of data.","url_abs":"http://arxiv.org/abs/1711.11293v2","url_pdf":"http://arxiv.org/pdf/1711.11293v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"parallel-data-free-voice-conversion-using","repo_url":"https://github.com/Jeongseungwoo/Singing-Style-transfer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"parallel-data-free-voice-conversion-using","repo_url":"https://github.com/daewon88/Korean-Voice-Conversion","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"parallel-data-free-voice-conversion-using","repo_url":"https://github.com/dipjyoti92/StarGAN-Voice-Conversion","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"parallel-data-free-voice-conversion-using","repo_url":"https://github.com/dipjyoti92/StarGAN-Voice-Conversion-2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"parallel-data-free-voice-conversion-using","repo_url":"https://github.com/jackaduma/CycleGAN-VC2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"parallel-data-free-voice-conversion-using","repo_url":"https://github.com/maratsubkhankulov/cyclegan-vc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"parallel-data-free-voice-conversion-using","repo_url":"https://github.com/softrime/CycleGAN-VC_Gluon","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mxnet","reach":{"status":"unanswered"}},{"paper_slug":"parallel-data-free-voice-conversion-using","repo_url":"https://github.com/softrime/cycleGAN-for-VC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mxnet","reach":{"status":"unanswered"}},{"paper_slug":"parallel-data-free-voice-conversion-using","repo_url":"https://github.com/nafiuny/ICRCycleGAN-VC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"voice-conversion","task_name":"Voice Conversion"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"cycle-consistency-loss","method_name":"Cycle Consistency Loss"},{"method_slug":"gan-least-squares-loss","method_name":"GAN Least Squares Loss"},{"method_slug":"instance-normalization","method_name":"Instance Normalization"},{"method_slug":"patchgan","method_name":"PatchGAN"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.11293","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.11293"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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