  Voice conversion methods have advanced rapidly over the last decade. Studies
have shown that speaker characteristics are captured by spectral feature as
well as various prosodic features. Most existing conversion methods focus on
the spectral feature as it directly represents the timbre characteristics,
while some conversion methods have focused only on the prosodic feature
represented by the fundamental frequency. In this paper, a comprehensive
framework using deep neural networks to convert both timbre and prosodic
features is proposed. The timbre feature is represented by a high-resolution
spectral feature. The prosodic features include F0, intensity and duration. It
is well known that DNN is useful as a tool to model high-dimensional features.
In this work, we show that DNN initialized by our proposed autoencoder
pretraining yields good quality DNN conversion models. This pretraining is
tailor-made for voice conversion and leverages on autoencoder to capture the
generic spectral shape of source speech. Additionally, our framework uses
segmental DNN models to capture the evolution of the prosodic features over
time. To reconstruct the converted speech, the spectral feature produced by the
DNN model is combined with the three prosodic features produced by the DNN
segmental models. Our experimental results show that the application of both
prosodic and high-resolution spectral features leads to quality converted
speech as measured by objective evaluation and subjective listening tests.
