Name
Abstract | ||
|---|---|---|
Segmenting the speech signals on the basis of time-frequency analysis is the most natural approach. Boundaries are located in places where energy of some frequency subband rapidly changes. Speech segmentation method which bases on discrete wavelet transform, the resulting power spectrum and its derivatives is presented. This information allows to locate the boundaries of phonemes. A statistical classification method was used to check which features are useful. The efficiency of segmentation was verified on a male speaker taken from a corpus of Polish language. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICME.2008.4607680 | 2008 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-4 |
Keywords | Field | DocType |
speech segmentation, WEKA, machine learning, classifier, LogitBoost | Scale-space segmentation,Pattern recognition,Computer science,Segmentation,Speech recognition,Discrete wavelet transform,Artificial intelligence,LogitBoost,Speech segmentation,Classifier (linguistics),Statistical classification,Wavelet transform | Conference |
Citations | PageRank | References |
1 | 0.35 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bartosz Ziólko | 1 | 46 | 15.76 |
| Suresh Manandhar | 2 | 1238 | 88.99 |
| Richard C. Wilson | 3 | 1754 | 137.60 |
| Mariusz Ziólko | 4 | 16 | 4.96 |