DNN-based cross-lingual voice conversion using Bottleneck Features
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.
Cite
@article{arxiv.1909.03974,
title = {DNN-based cross-lingual voice conversion using Bottleneck Features},
author = {M Kiran Reddy and K Sreenivasa Rao},
journal= {arXiv preprint arXiv:1909.03974},
year = {2019}
}