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Restricted Boltzmann Machine-Based Voice Conversion for Nonparallel Corpus
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DOI：10.1109/LSP.2017.2713412.png)
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摘要
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A large amount of parallel training corpus is necessary for robust, high-quality voice conversion. However, such parallel data may not always be available. This letter presents a new voice conversion method that needs no parallel speech corpus, and adopts a restricted Boltzmann machine (RBM) to represent the distribution of the spectral features derived from a target speaker. A linear transformation was employed to convert the spectral and delta features. A conversion function was obtained by maximizing the conditional probability density function with respect to the target RBM. A feasibility test was carried out on the OGI VOICES corpus. Results from the subjective listening tests and the objective results both showed that the proposed method outperforms the conventional GMM-based method.
Keyword:
Linear transformation
restricted Boltzmann machine (RBM)
voice conversion (VC)
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期刊
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9.6
论文数:
1.1W
被引数:
1.7W
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