Paper Info
Title
Activity-Mapping Non-Negative Matrix Factorization For Exemplar-Based Voice Conversion
Abstract
Voice conversion (VC) is being widely researched in the field of speech processing because of increased interest in using such processing in applications such as personalized Text-To-Speech systems. We present in this paper an exemplar-based VC method using Non-negative Matrix Factorization (NMF), which is different from conventional statistical VC. In our previous exemplar-based VC method, input speech is represented by the source dictionary and its sparse coefficients. The source and the target dictionaries are fully coupled and the converted voice is constructed from the source coefficients and the target dictionary. In this paper, we propose an Activity-mapping NMF approach and introduce mapping matrices between source and target sparse coefficients. The effectiveness of this method was confirmed by comparing its effectiveness with that of a conventional Gaussian Mixture Model (GMM)-based method and a conventional NMF-based method.
Year
Venue
Keywords
2015
2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP)
voice conversion, sparse representation, non-negative matrix factorization, NMF
Field
DocType
ISSN
Training set,Speech processing,Pattern recognition,Computer science,Matrix (mathematics),Matrix decomposition,Speech recognition,Non-negative matrix factorization,Artificial intelligence,Matrix converters,Mixture model,Sparse matrix
Conference
1520-6149
Citations 
PageRank 
References 
4
0.39
16
Authors
3
Name
Order
Citations
PageRank
Ryo Aihara1315.44
Tetsuya Takiguchi2858.77
Yasuo Ariki351988.94