基于i向量和变分自编码相对生成对抗网络的语音转换

李燕萍; 曹盼; 左宇涛; 张燕; 钱博

doi:10.16383/j.aas.c190733

基于i向量和变分自编码相对生成对抗网络的语音转换

doi: 10.16383/j.aas.c190733

李燕萍^1,,
曹盼^1,,
左宇涛^1,,
张燕^2,,
钱博^3,

1.
南京邮电大学通信与信息工程学院南京 210003
2.
金陵科技学院南京 211169
3.
南京电子技术研究所南京 210039

基金项目: 国家自然科学青年基金(61401227), 国家自然科学面上基金(61872199, 61872424), 金陵科技学院智能人机交互科技创新团队建设专项(218/010119200113)资助

详细信息

作者简介:
李燕萍：南京邮电大学通信与信息工程学院副教授. 2009年获得南京理工大学博士学位. 主要研究方向为语音转换和说话人识别. E-mail: liyp@njupt.edu.cn

曹盼：南京邮电大学通信与信息工程学院研究生. 2020年获得南京邮电大学硕士学位.主要研究方向为语音转换和深度学习. E-mail: abreastpc@163.com

左宇涛：南京邮电大学通信与信息工程学院研究生. 2019年获得南京邮电大学硕士学位. 主要研究方向为语音转换. E-mail: zuoyt@chinatelecom.cn

张燕：金陵科技学院软件工程学院教授. 2017年获得南京理工大学博士学位. 主要研究方向为模式识别和领域软件工程. E-mail: zy@jit.edu.cn

钱博：南京电子技术研究所高级工程师. 2007年获得南京理工大学博士学位. 主要研究方向为模式识别和人工智能. E-mail: sandson6@163.com

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- 文章访问数: 16
- HTML全文浏览量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2019-10-23
- 录用日期: 2020-07-27

Voice Conversion based on i-vector with Variational Autoencoding Relativistic Standard Generative Adversarial Network

LI Yan-Ping^1
,,
CAO Pan^1
,,
ZUO Yu-Tao^1
,,
ZHANG Yan^2
,,
QIAN Bo^3
,

1.
School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003
2.
Jinling Institute of Technology, Nanjing 211169
3.
Nanjing Institute of Electronic Technology, Nanjing 210039

Funds: Supported by National Natural Science Foundation of Youth Foundation of China (61401227), National Natural Science Foundation of China (61872199, 61872424), and Special Project of Intelligent Human-Computer Interaction Technology Innovation Team Building of Jinling Institute of Technology (218/010119200113)

摘要

摘要: 该文提出一种基于i 向量和变分自编码相对生成对抗网络的语音转换方法, 实现了非平行文本条件下高质量的多对多语音转换. 性能良好的语音转换系统, 既要保持重构语音的自然度, 又要兼顾转换语音的说话人个性特征是否准确. 首先为了改善合成语音自然度, 利用生成性能更好的相对生成对抗网络代替基于变分自编码生成对抗网络模型中的Wasserstein生成对抗网络, 通过构造相对鉴别器的方式, 使得鉴别器的输出依赖于真实样本和生成样本间的相对值, 克服了Wasserstein生成对抗网络性能不稳定和收敛速度较慢等问题. 进一步为了提升转换语音的说话人个性相似度, 在解码阶段, 引入含有丰富个性信息的i-vector, 以充分学习说话人的个性化特征. 客观和主观实验表明, 转换后的语音平均MCD值较基准模型降低4.80%, MOS 值提升5.12%, ABX 值提升8.60%, 验证了该方法在语音自然度和个性相似度两个方面均有显著的提高, 实现了高质量的语音转换.
- 语音转换 /
- 相对生成对抗网络 /
- i向量 /
- 非平行文本 /
- 变分自编码器 /
- 多对多
Abstract: This paper proposes a novel voice conversion (VC) method based on i-vector and variational autoencoding relativistic standard generative adversarial network, which can realize high-quality many-to-many voice conversion for non-parallel corpora. A high performance voice conversion method should not only ensure speech naturalness, but also take into account speaker similarity of converted speech. Firstly, in order to improve the speech naturalness, the Wasserstein generative adversarial network (WGAN) in the voice conversion model based on variational autoencoding generative adversarial network is replaced by the relativistic standard generative adversarial network (RSGAN), which makes the output of the discriminator depend on the relativistic standard value between real and generated sample by constructing a relativistic standard discriminator, overcoming the unstable performance and slow convergence rate. Furthermore, i-vector representing speaker characteristics is adopted as speaker representation for many-to-many VC in addition to traditional one- hot vector, thus significantly improving speaker similarity of converted speech. Sufficient objective and subjective experiments show that the average value of MCD is decreased by 4.80%, the MOS is increased by 5.12%, and ABX is increased by 8.60% compared with baseline method VAWGAN, which demonstrate that the proposed method has a great improvement in both speech naturalness and speaker similarity.
- Voice conversion /
- relativistic standard generative adversarial network /
- i-vector /
- non-parallel corpora /
- variational autoencoder /
- many-to-many

HTML全文

图 1 基于VARSGAN+i-vector 模型的整体流程图

Fig. 1 Framework of voice conversion based on VARSGAN+i-vector network

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图 2 VARSGAN+i-vector 模型原理示意图

Fig. 2 Schematic diagram of VARSGAN+i-vector network

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图 3 VARSGAN+i-vector 模型网络结构示意图

Fig. 3 Structure of VARSGAN+i-vector network

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图 4 16 种转换情形下五种模型的转换语音的MCD值对比

Fig. 4 Average MCD of five models for 16 conversion cases

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图 5 四类转换情形下不同模型的MCD值对比

Fig. 5 Comparison of MCD of different models for four conversion cases

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图 6 五种模型在不同转换类别下的MOS值对比

Fig. 6 Comparison of MOS for different conversion categories in five models

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图 7 同性转换情形下五种模型转换语音的ABX图

Fig. 7 ABX test results of five models for intra-gender

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图 8 异性转换情形下五种模型转换语音的ABX图

Fig. 8 ABX test results of five models for inter-gender

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