Name
Abstract | ||
|---|---|---|
Classical parameterization techniques for Speaker Identifi- cation use the codification of the power spectral density of raw speech, not discriminating between articulatory features produced by vocal tract dynamics (acoustic-phonetics) from glottal source biometry. Through the present paper a study is conducted to separate voicing fragments of speech into vo- cal and glottal components, dominated respectively by the vocal tract transfer function estimated adaptively to track the acoustic-phonetic sequence of the message, and by the glottal characteristics of the speaker and the phonation ges- ture. The separation methodology is based in Joint Process Estimation under the uncorrelation hypothesis between vo- cal and glottal spectral distributions. Its application on voiced speech is presented in the time and frequency do- mains. The parameterization methodology is also described. Speaker Identification experiments conducted on 245 speak- ers are shown comparing different parameterization strate- gies. The results confirm the better performance of de- coupled parameterization compared against approaches based on plain speech parameterization. |
Year | Venue | Field |
|---|---|---|
2008 | European Signal Processing Conference | Speaker identification,Parametrization,Gesture,Computer science,Speech recognition,Transfer function,Spectral density,Voice,Phonation,Vocal tract |
DocType | ISSN | Citations |
Conference | 2219-5491 | 2 |
PageRank | References | Authors |
0.41 | 8 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Gómez | 1 | 5 | 2.10 |
| A. Alvarez | 2 | 2 | 0.41 |
| Luis Miguel Mazaira-Fernández | 3 | 2 | 0.75 |
| R. Fernandez | 4 | 2 | 0.41 |
| Victor Nieto Lluis | 5 | 2 | 0.41 |
| R. Martínez | 6 | 29 | 13.46 |
| C. Muñoz | 7 | 2 | 0.75 |
| V. Rodellar | 8 | 14 | 7.98 |