Sentence examples for voiced segmentation from inspiring English sources

Unvoiced segmentation is based on onset/offset analysis as mentioned in Section 2.2 while voiced segmentation is mainly based on the extracted features such as response energy and cross-channel correlation.

Here, the authors proposed an algorithm based on an automatic voice segmentation of speech signals to detect voiced segments and a spectral matching approach to estimate pitch and pitch changes.

However, voice production allows smooth transitions between voiced/unvoiced states which makes voicing segmentation sometimes tricky to estimate.

Finally, the proposed approach has the inherent advantage that the segmentation of the speech into turns is not required since the system carries out an implicit segment-by-segment modelling of voiced speech segments.

These features avoid voiced and unvoiced segmentation.

In the segmentation stage, both voiced and unvoiced segments are generated.

For unvoiced speech segregation, Hu Wang system employed a multi-scale onset and offset analysis for unvoiced speech segmentation, which makes both voiced and unvoiced speeches correctly segmented in[8].

The use of only voiced frames for performing speaker segmentation, which was implemented in one of the systems, seems a very interesting step in context of the very good speaker error result of that particular system.

The analysis of speaker diarization results and the characteristics of the submitted systems revealed several observations which can be summarized as follows: The use of only voiced frames for performing speaker segmentation, which was implemented in one of the systems, seems a very interesting step in context of the very good speaker error result of that particular system.

Segmentation stage comprises two parts, namely voiced and unvoiced.

This is because we want to benefit from the V/UV signal segmentation to improve the quantization process by better fitting the general trends of voiced or unvoiced LSFs.