@InProceedings{Sinha2022_1164,
<br/>   author = {Yamini Sinha and Andreas Wendemuth and Ingo Siegert},
<br/>   booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2022},
<br/>   title = {Emotion preservation for one-shot speaker anonymization using McAdams},
<br/>   year = {2022},
<br/>   editor = {Oliver Niebuhr and Malin Svensson Lundmark and Heather Weston},
<br/>   month = mar,
<br/>   pages = {235--242},
<br/>   publisher = {TUDpress, Dresden},
<br/>   abstract  = {With the widespread use of voice-assistant, a lot of effort has been made
by researchers to improve the usage of these systems. Especially speech recognition
and speech understanding are in the focus of research but also the analyses of
prosodic information, e.g. emotion recognition is just around the corner.
Similarly, a growing concern for (speech) data privacy arises. Speech data consists
of information that can be used to identify a speaker, such as gender, emotional state,
and thus a stronger need for protection against misuse is arising. Together with
the requirement of easy-to-use anonymization that fits into the easy use of existing
voice assistants, the current paper analyses the McAdams anonymization technique,
as an approach that can be used without any pre-training. Using a highly expressive
German speech database, the performance regarding anonymity, automatic speech
recognition performance, and emotion preservation for different parameter ranges
of McAdams are analyzed.},
<br/>   isbn = {978-3-95908-548-9},
<br/>   issn = {0940-6832},
<br/>   keywords = {Emotion},
<br/>   url = {https://www.essv.de/pdf/2022_235_242.pdf},
<br/>}