Multiple Non-Negative Matrix Factorization for Many-to-Many Voice Conversion
| Autor: | Tetsuya Takiguchi, Ryo Aihara, Yasuo Ariki |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Acoustics and Ultrasonics
Computer science Speech recognition Speech synthesis 02 engineering and technology computer.software_genre Non-negative matrix factorization Matrix decomposition 030507 speech-language pathology & audiology 03 medical and health sciences Naturalness Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) Electrical and Electronic Engineering Sparse matrix Training set business.industry Pattern recognition Mixture model Computational Mathematics 020201 artificial intelligence & image processing Artificial intelligence 0305 other medical science business computer |
| Zdroj: | IEEE/ACM Transactions on Audio, Speech, and Language Processing. 24:1175-1184 |
| ISSN: | 2329-9304 2329-9290 |
| DOI: | 10.1109/taslp.2016.2522643 |
| Popis: | A novel voice conversion (VC) method for arbitrary speakers is proposed. Non-negative matrix factorization (NMF) has recently been applied to exemplar-based VC. It offers noise robustness and naturalness of the converted voice, compared with widely used Gaussian mixture model-based VC. However, because NMF-based VC requires parallel training data from source and target speakers, the voice of arbitrary speakers cannot be converted in this framework. In this study, we propose the multiple non-negative matrix factorization (Multi-NMF) to allow the implementation of many-to-many, exemplar-based VC. Our experimental results demonstrate that the conversion quality of the proposed method is close to that of conventional one-to-one VC, even though the proposed method requires neither the source speakers' spectra, nor the target speakers' spectra, to be included in the training set. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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