Paper Info
Title
Error Reduction Network for DBLSTM-based Voice Conversion.
Abstract
So far, many of the deep learning approaches for voice conversion produce good quality speech by using a large amount of training data. This paper presents a Deep Bidirectional Long Short-Term Memory (DBLSTM) based voice conversion framework that can work with a limited amount of training data. We propose to implement a DBLSTM based average model that is trained with data from many speakers. Then, we propose to perform adaptation with a limited amount of target data. Last but not least, we propose an error reduction network that can improve the voice conversion quality even further. The proposed framework is motivated by three observations. Firstly, DBLSTM can achieve a remarkable voice conversion by considering the long-term dependencies of the speech utterance. Secondly, DBLSTM based average model can be easily adapted with a small amount of data, to achieve a speech that sounds closer to the target. Thirdly, an error reduction network can be trained with a small amount of training data, and can improve the conversion quality effectively. The experiments show that the proposed voice conversion framework is flexible to work with limited training data and outperforms the traditional frameworks in both objective and subjective evaluations.
Year
DOI
Venue
2018
10.23919/APSIPA.2018.8659543
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
Field
DocType
ISSN
Training set,Data modeling,Mel-frequency cepstrum,Computer science,Utterance,Speech recognition,Feature extraction,Artificial intelligence,Deep learning
Conference
2309-9402
Citations 
PageRank 
References 
2
0.35
0
Authors
5
Name
Order
Citations
PageRank
Mingyang Zhang110410.61
Berrak Sisman26010.34
Sai Sirisha Rallabandi320.35
Haizhou Li43678334.61
Li Zhao519822.70