@InProceedings{Mousavi2024_1227,
<br/>   author = {Neda Mousavi and Sven Grawunder},
<br/>   booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2024},
<br/>   title = {The Influence of Signal Segmentation Methods on Rhythm-Based Speaker Recognition},
<br/>   year = {2024},
<br/>   editor = {Timo Baumann},
<br/>   month = mar,
<br/>   pages = {225--232},
<br/>   publisher = {TUDpress, Dresden},
<br/>   abstract  = {This study investigates the effects of speech segmentation methods on
speaker recognition models, particularly with regard to the use of rhythmic feature
sets. Using three automatic methods and one manual method on the German
database of Kiel corpus, segmentation was performed based on the identification of
vowel onsets. Subsequently rhythmic variability indices derived from these intervals
were calculated and used for principal component analysis and support vector
machine model in order to investigate the variation among speakers. The results underline
the influence of signal segmentation methods on speaker recognition models.},
<br/>   isbn = {978-3-95908-325-6},
<br/>   issn = {0940-6832},
<br/>   keywords = {Poster},
<br/>   url = {https://www.essv.de/pdf/2024_225_232.pdf},
<br/>   doi = {10.35096/othr/pub-7101},
<br/>}