Audiovisual speaker conversion: jointly and simultaneously transforming facial expression and acoustic characteristics
This addresses the challenge of creating natural audiovisual content for applications like entertainment or virtual communication, though it is incremental as it builds on existing speaker conversion techniques.
The paper tackled the problem of simultaneously transforming both facial expressions and voice of a source speaker to match a target speaker, achieving significantly higher naturalness compared to methods that handle these features separately.
An audiovisual speaker conversion method is presented for simultaneously transforming the facial expressions and voice of a source speaker into those of a target speaker. Transforming the facial and acoustic features together makes it possible for the converted voice and facial expressions to be highly correlated and for the generated target speaker to appear and sound natural. It uses three neural networks: a conversion network that fuses and transforms the facial and acoustic features, a waveform generation network that produces the waveform from both the converted facial and acoustic features, and an image reconstruction network that outputs an RGB facial image also based on both the converted features. The results of experiments using an emotional audiovisual database showed that the proposed method achieved significantly higher naturalness compared with one that separately transformed acoustic and facial features.