@InProceedings{Mousavi2024_1228,
<br/>   author = {Neda Mousavi and Seyyed Saeed Sarfjoo and Sven Grawunder},
<br/>   booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2024},
<br/>   title = {Unsupervised Emotional Pattern Recognition Using Rhythmic and Vocal Features},
<br/>   year = {2024},
<br/>   editor = {Timo Baumann},
<br/>   month = mar,
<br/>   pages = {233--240},
<br/>   publisher = {TUDpress, Dresden},
<br/>   abstract  = {In this study, we address the complex dynamics of emotional speech and
comprehensively examine the integration of rhythmic and vocal features to recognize
emotional patterns. Our exploration is conducted using two German emotional
corpora: VMEmo and EmoDB. Employing a combination of supervised methods
(here linear discriminant analysis, LDA) and unsupervised techniques (here kmeans
clustering), we aim to uncover nuanced patterns within the emotional speech
in these corpora. The application of LDA highlights salient patterns across different
feature sets and focuses on the classification of speakers and prosodic characteristics.
In addition, k-means clustering uncovers latent structures that reveal subtle
mapping between emotions and speech behavior. Our results suggest that it is possible
to cluster data based on prosodic behaviors that are influenced by emotional
changes. Although precise mapping to the actual clusters derived from emotional
labels could not be fully achieved, the results nonetheless reveal a moderate level
of success in this investigation.},
<br/>   isbn = {978-3-95908-325-6},
<br/>   issn = {0940-6832},
<br/>   keywords = {Poster},
<br/>   url = {https://www.essv.de/pdf/2024_233_240.pdf},
<br/>   doi = {10.35096/othr/pub-7102},
<br/>}