  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.
