@InProceedings{Mousavi2025_1262,
<br/>   author = {Neda Mousavi and Sven Grawunder},
<br/>   booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2025},
<br/>   title = {An unsupervised approach to exploring speaking task complexity based on fluency metrics},
<br/>   year = {2025},
<br/>   editor = {Sven Grawunder},
<br/>   month = mar,
<br/>   pages = {273--280},
<br/>   publisher = {TUDpress, Dresden},
<br/>   abstract  = {In this study, task complexity is investigated by analyzing the fluency
metrics extracted based on the temporal patterns of verbal and non-verbal elements
in two languages. The data include a speech corpus of 60 participants (30 Persian
and 30 German speakers) who completed seven different speaking tasks, including
reading formal and informal texts, spontaneous conversations, describing pictures,
telling stories, and leaving formal and informal messages. The recorded audio files
were annotated using four labels - speech, pause, filler and repair - and 19 metrics
for fluency were extracted based on duration, number, rate, and ratio of intervals.
In the first step, principal component analysis revealed differences between the two
languages in how tasks were distributed in the PCA space. This was followed by KMeans clustering, applied as an unsupervised method to identify hidden patterns,
which were interpreted in relation to task complexity. The model identified four
complexity clusters in both languages, with distinct distribution patterns. German
speakers exhibited a more structured clustering, indicating greater adaptation to
task demands, while Persian speakers showed a less regular distribution, suggesting
weaker adherence to task genres and potentially greater individual variability.},
<br/>   isbn = {978-3-95908-803-9},
<br/>   issn = {0940-6832},
<br/>   keywords = {Poster},
<br/>   url = {https://www.essv.de/pdf/2025_273_280.pdf},
<br/>}