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{
"abstracts": [
{
"content": "This paper introduces Taco-VC, a novel architecture for voice conversion\nbased on Tacotron synthesizer, which is a sequence-to-sequence with attention\nmodel. The training of multi-speaker voice conversion systems requires a large\nnumber of resources, both in training and corpus size. Taco-VC is implemented\nusing a single speaker Tacotron synthesizer based on Phonetic PosteriorGrams\n(PPGs) and a single speaker WaveNet vocoder conditioned on mel spectrograms. To\nenhance the converted speech quality, and to overcome over-smoothing, the\noutputs of Tacotron are passed through a novel speechenhancement network, which\nis composed of a combination of the phoneme recognition and Tacotron networks.\nOur system is trained just with a single speaker corpus and adapts to new\nspeakers using only a few minutes of training data. Using mid-size public\ndatasets, our method outperforms the baseline in the VCC 2018 SPOKE\nnon-parallel voice conversion task and achieves competitive results compared to\nmulti-speaker networks trained on large private datasets.",
"lang": "en",
"mimetype": "text/plain",
"sha1": "3d39e2b0529feac401b47d8f5c048e27be0e9b60"
}
],
"contribs": [
{
"index": 0,
"raw_name": "Roee Levy Leshem",
"role": "author"
},
{
"index": 1,
"raw_name": "Raja Giryes",
"role": "author"
}
],
"ext_ids": {
"arxiv": "1904.03522v4"
},
"extra": {
"arxiv": {
"base_id": "1904.03522",
"categories": [
"cs.SD",
"cs.LG",
"eess.AS"
],
"comments": "Accepted to EUSIPCO 2020"
}
},
"ident": "efumvvpw6jbb7ehp2qfdatgxzy",
"language": "en",
"license_slug": "ARXIV-1.0",
"refs": [],
"release_date": "2020-06-19",
"release_stage": "submitted",
"release_type": "article",
"release_year": 2020,
"revision": "32b1f508-d004-47dc-bc1a-2a65feb3a1a7",
"state": "active",
"title": "Taco-VC: A Single Speaker Tacotron based Voice Conversion with Limited Data",
"version": "v4",
"work_id": "bqizapjfrfbbhnele4mba3e5ay"
}
|
