Abstract
The need for a reliable discrimination among
voiced, unvoiced and silence frames arises in a wide variety of
speech processing applications. In this paper, we propose an
unsupervised algorithm for voiced-unvoiced-silence classification
based on a time-frequency representation of the measured signal,
which is viewed as a data matrix. The proposed algorithm relies
on a hierarchical dual geometry analysis of the data matrix,
which exploits the strong coupling between time frames and
frequency bins. By gradually learning the coupled geometry in
two steps, the algorithm allows for the separation between speech
and silent frames, and then between voiced and unvoiced frames.
Experimental results demonstrate the improved performance
compared to a competing algorithm
voiced, unvoiced and silence frames arises in a wide variety of
speech processing applications. In this paper, we propose an
unsupervised algorithm for voiced-unvoiced-silence classification
based on a time-frequency representation of the measured signal,
which is viewed as a data matrix. The proposed algorithm relies
on a hierarchical dual geometry analysis of the data matrix,
which exploits the strong coupling between time frames and
frequency bins. By gradually learning the coupled geometry in
two steps, the algorithm allows for the separation between speech
and silent frames, and then between voiced and unvoiced frames.
Experimental results demonstrate the improved performance
compared to a competing algorithm
| Original language | American English |
|---|---|
| Title of host publication | ISCEE International Conference on the Science of Electrical Engineering 2016 |
| Pages | 75 |
| State | Published - 2016 |