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-{
-  "abstracts": [
-    {
-      "content": "This paper introduces Taco-VC, a novel architecture for voice conversion (VC)\nbased on the Tacotron synthesizer, which is a sequence-to-sequence with\nattention model. Most current prosody preserving VC systems suffer from target\nsimilarity and quality issues in the converted speech. To address these\nproblems, we first recover initial prosody preserving speech using a Phonetic\nPosteriorgrams (PPGs) based Tacotron synthesizer. Then, we enhance the quality\nof the converted speech using a novel speech-enhancement network, which is\nbased on a combination of phoneme recognition and Tacotron networks. The final\nconverted speech is generated by a Wavenet vocoder conditioned on Mel\nSpectrograms. Given the advantages of a single speaker Tacotron and Wavenet, we\nshow how to adapt them to other speakers with limited training data. We\nevaluate our solution on the VCC 2018 SPOKE task. Using public mid-size\ndatasets, our method outperforms the baseline and achieves competitive results",
-      "lang": "en",
-      "mimetype": "text/plain",
-      "sha1": "6b3ec27cabf8042bb5dd6278b9aa0cee71d92789"
-    }
-  ],
-  "contribs": [
-    {
-      "index": 0,
-      "raw_name": "Roee Levy Leshem",
-      "role": "author"
-    },
-    {
-      "index": 1,
-      "raw_name": "Raja Giryes",
-      "role": "author"
-    }
-  ],
-  "ext_ids": {
-    "arxiv": "1904.03522v1"
-  },
-  "extra": {
-    "arxiv": {
-      "base_id": "1904.03522",
-      "categories": [
-        "cs.SD",
-        "cs.LG",
-        "eess.AS"
-      ],
-      "comments": "Submitted to Interspeech 2019"
-    },
-    "superceded": true
-  },
-  "ident": "funn7cwjbrgefji27tzpl4avuu",
-  "language": "en",
-  "license_slug": "ARXIV-1.0",
-  "refs": [],
-  "release_date": "2019-04-06",
-  "release_stage": "submitted",
-  "release_type": "article",
-  "release_year": 2019,
-  "revision": "8dd55bc1-c846-48f9-bcc6-96069460b8d8",
-  "state": "active",
-  "title": "Taco-VC: A Single Speaker Tacotron based Voice Conversion with Limited\n  Data",
-  "version": "v1",
-  "work_id": "bqizapjfrfbbhnele4mba3e5ay"
-}