Sentence examples for source speaker from inspiring English sources

(a) CRBMs for a source speaker (below) and a target speaker.

As a result, the synthesized output has the same speaking rate as the source speaker.

The source speaker was a Japanese male, and the target speaker was a Japanese female.

As Equation 24 indicates, we need a current acoustic vector from a source speaker and previous vectors from both a source speaker and a target speaker to estimate the target speaker's current acoustic vector.

Therefore, feature vectors of source speaker are time aligned with that of the target speaker to train the mapping model.

Aligned audio and visual features of the source speaker are joined and used as a source feature.

from Equation 17, where c x is a bias vector of forward inference for the source speaker.

This paper presents a voice transformation algorithm which modifies the speech of a source speaker such that it is perceived as if spoken by a target speaker.

Radial Basis Function is explored to establish the nonlinear mapping rules for modifying the source speaker features to that of the target speaker.

Subsequently, the source speaker characteristics are transformed to that of target speaker using mapping function developed in the training phase[3].

Voice conversion systems aim to modify the perceived identity of a source speaker saying a sentence to that of a given target speaker.