Authors:
Yusuke Sekii
1
;
Ryohei Orihara
1
;
Keisuke Kojima
2
;
Yuichi Sei
1
;
Yasuyuki Tahara
1
and
Akihiko Ohsuga
1
Affiliations:
1
University of Electro-Communications, Japan
;
2
Solid Sphere and inc., Japan
Keyword(s):
Voice Conversion, Autoencoder, Deep Learning, Deep Neural Network, Spectral Envelope.
Related
Ontology
Subjects/Areas/Topics:
AI and Creativity
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
;
Theory and Methods
Abstract:
Most of voice conversion (VC) methods were dealing with a one-to-one VC issue and there were few studies
that tackled many-to-one / many-to-many cases. It is difficult to prepare the training data for an application
with the methods because they require a lot of parallel data. Furthermore, the length of time required to convert
a speech by Deep Neural Network (DNN) gets longer than pre-DNN methods because the DNN-based
methods use complicated networks. In this study, we propose a VC method using autoencoders in order to
reduce the amount of the training data and to shorten the converting time. In the method, higher-order features
are extracted from acoustic features of source speakers by an autoencoder trained with source speakers’ data.
Then they are converted to higher-order features of a target speaker by DNN. The converted higher-order features
are restored to the acoustic features by an autoencoder trained with data drawn from the target speaker. In
the evaluation exper
iment, the proposed method outperforms the conventional VC methods that use Gaussian
Mixture Models (GMM) and DNNs in both one-to-one conversion and many-to-one conversion with a small
training set in terms of the conversion accuracy and the converting time.
(More)