1 research outputs found
Voice conversion using coefficient mapping and neural network
The research presents a voice conversion model using coefficient mapping and
neural network. Most previous works on parametric speech synthesis did not
account for losses in spectral details causing over smoothing and invariably,
an appreciable deviation of the converted speech from the targeted speaker. An
improved model that uses both linear predictive coding (LPC) and line spectral
frequency (LSF) coefficients to parametrize the source speech signal was
developed in this work to reveal the effect of over-smoothing. Non-linear
mapping ability of neural network was employed in mapping the source speech
vectors into the acoustic vector space of the target. Training LPC coefficients
with neural network yielded a poor result due to the instability of the LPC
filter poles. The LPC coefficients were converted to line spectral frequency
coefficients before been trained with a 3-layer neural network. The algorithm
was tested with noisy data with the result evaluated using Mel-Cepstral
Distance measurement. Cepstral distance evaluation shows a 35.7 percent
reduction in the spectral distance between the target and the converted speech.Comment: 5 page