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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+    "superceded": true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"
+}