  This research presents a neural network based voice conversion (VC) model.
While it is a known fact that voiced sounds and prosody are the most important
component of the voice conversion framework, what is not known is their
objective contributions particularly in a noisy and uncontrolled environment.
This model uses a 2-layer feedforward neural network to map the Linear
prediction analysis coefficients of a source speaker to the acoustic vector
space of the target speaker with a view to objectively determine the
contributions of the voiced, unvoiced and supra-segmental components of sounds
to the voice conversion model. Results showed that vowels 'a', 'i', 'o' have
the most significant contribution in the conversion success. The voiceless
sounds were also found to be most affected by the noisy training data. An
average noise level of 40 dB above the noise floor were found to degrade the
voice conversion success by 55.14 percent relative to the voiced sounds. The
result also shows that for cross-gender voice conversion, prosody conversion is
more significant in scenarios where a female is the target speaker.
