@InProceedings{Requardt2020_456,
<br/>   author = {Alicia F. Requardt and Olga Egorow and Andreas Wendemuth},
<br/>   booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2020},
<br/>   title = {Machine Learning-Assisted Affect Labelling of Speech Data},
<br/>   year = {2020},
<br/>   editor = {Andreas Wendemuth and Ronald Böck and Ingo Siegert},
<br/>   month = mar,
<br/>   pages = {199--205},
<br/>   publisher = {TUDpress, Dresden},
<br/>   abstract  = {This paper addresses the assisted annotation of emotions in affective
speech data recorded in natural “in the wild” surroundings. Here, affective states
with low expressiveness are encountered which makes manual annotation difficult
and very time-consuming even for expert human annotators. Further, the training
of an automatic emotion recognition system in such a setup requires high amounts
of annotated data. We present a machine-learning-assisted semi-automatic annotation
procedure, adopted from speech recognition. We give annotation time estimates
and evaluate our approach on data of real-life in-vehicle emotions which
are prototypical for natural surroundings. The time necessary for the complete data
annotation could be substantially reduced to around 80% of the time needed for
the fully manual annotation. At the same time, the quality of the obtained annotation
remains the same as of the fully manual approach, in contrast to other
currently available approaches such as Active Learning or Semi-Supervised Learning.
Having shown the time saving effect, our approach is generally highly useful
for annotation processes with high annotation effort.},
<br/>   isbn = {978-3-959081-93-1},
<br/>   issn = {0940-6832},
<br/>   keywords = {Poster},
<br/>   url = {https://www.essv.de/pdf/2020_199_205.pdf},
<br/>}