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arXiv · 1909.03974

DNN-based cross-lingual voice conversion using Bottleneck Features

M Kiran Reddy·K Sreenivasa Rao
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Abstract

Cross-lingual voice conversion (CLVC) is a quite challenging task since the source and target speakers speak different languages. This paper proposes a CLVC framework based on bottleneck features and deep neural network (DNN). In the proposed method, the bottleneck features extracted from a deep auto-encoder (DAE) are used to represent speaker-independent features of speech signals from different languages. A DNN model is trained to learn the mapping between bottleneck features and the corresponding spectral features of the target speaker. The proposed method can capture speaker-specific characteristics of a target speaker, and hence requires no speech data from source speaker during training. The performance of the proposed method is evaluated using data from three Indian languages: Telugu, Tamil and Malayalam. The experimental results show that the proposed method outperforms the baseline Gaussian mixture model (GMM)-based CLVC approach.

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BibTeXRIS

M Kiran Reddy, K Sreenivasa Rao. 2019-09-10. DNN-based cross-lingual voice conversion using Bottleneck Features. https://doi.org/10.1007/s11063-019-10149-y

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