Modeling a Noisy-channel for Voice Conversion Using Articulatory Features

In this paper, we propose modeling a noisy-channel for the task of voice conversion (VC). We have used the artificial neural networks (ANN) to capture speaker-specific characteristics of a target speaker which avoid the need for any training utterance from a source speaker. We use articulatory features (AFs) as a canonical form or speaker-independent representation of a speech signal. Our studies show that AFs contain a significant amount of speaker information in their trajectories. Suitable techniques are proposed to normalize the speaker-specific information in AF trajectories and the resultant AFs are used in voice conversion. The results of voice conversion evaluated using objective and subjective measures confirm that AFs can be used as a canonical form in nosiy-channel to capture speakerspecific characteristics of a target speaker.