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The NU Voice Conversion System for the Voice Conversion Challenge 2020:
  On the Effectiveness of Sequence-to-sequence Models and Autoregressive Neural
  Vocoders

The NU Voice Conversion System for the Voice Conversion Challenge 2020: On the Effectiveness of Sequence-to-sequence Models and Autoregressive Neural Vocoders

9 October 2020
Wen-Chin Huang
Patrick Lumban Tobing
Yi-Chiao Wu
Kazuhiro Kobayashi
T. Toda
ArXivPDFHTML

Papers citing "The NU Voice Conversion System for the Voice Conversion Challenge 2020: On the Effectiveness of Sequence-to-sequence Models and Autoregressive Neural Vocoders"

3 / 3 papers shown
Title
Two-stage training method for Japanese electrolaryngeal speech
  enhancement based on sequence-to-sequence voice conversion
Two-stage training method for Japanese electrolaryngeal speech enhancement based on sequence-to-sequence voice conversion
D. Ma
Lester Phillip Violeta
Kazuhiro Kobayashi
T. Toda
21
6
0
19 Oct 2022
High Fidelity Speech Synthesis with Adversarial Networks
High Fidelity Speech Synthesis with Adversarial Networks
Mikolaj Binkowski
Jeff Donahue
Sander Dieleman
Aidan Clark
Erich Elsen
Norman Casagrande
Luis C. Cobo
Karen Simonyan
223
239
0
25 Sep 2019
Effective Approaches to Attention-based Neural Machine Translation
Effective Approaches to Attention-based Neural Machine Translation
Thang Luong
Hieu H. Pham
Christopher D. Manning
218
7,926
0
17 Aug 2015
1