Sentence examples for target voice from inspiring English sources

CTT is grounded in motor learning theory, focused on training target voice goals in spontaneous, conversational speech in the first session and throughout.

With STRAIGHT, we used converted spectra, F0, and source aperiodic components for synthesizing the target voice.

Our proposed framework, as shown in Fig. 6, transforms both the excitation and the filter features from the source voice to the target voice.

Then, we used these MCC features of the source and target voice as the input-layer data and output-layer data for DBNs.

Table 5 presents the recognition rates for the six target voices, for nonadapted, adapted to singing, and adapted to target voice systems.

The adaptation to singing was carried out in a one-pass global step, using all the singing material from except the target voice.

The resulting voice conversion method can be used to visualize the differences between source and target voices or styles in terms of formant location in frequency, spectral tilt and amplitude in a number of spectral bands.

VCs usually require a small amount of target data to synthesize multiple target voices.

These methods can synthesize target voices without over-smoothness with a small amount of the original target data.

In the second stage, we first transformed aligned spectral features of source and target voices to 24-dimensional MCC features.

Comparing the results of the baseline for synthetic versus natural target voices, the PESQ median reduced ≈14%% for female and increased the same percentage for male speakers.