@InProceedings{Schubert2025_1264,
<br/>   author = {Martha Schubert and Matthias Busch and Julia Krüger and Ingo Siegert},
<br/>   booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2025},
<br/>   title = {Speech technology in psychotherapy: exploring transcription tools and their potential impact},
<br/>   year = {2025},
<br/>   editor = {Sven Grawunder},
<br/>   month = mar,
<br/>   pages = {289--296},
<br/>   publisher = {TUDpress, Dresden},
<br/>   abstract  = {Psychotherapy is an inherently complex process, where keeping track
of the multitude of different conversations a therapist engages in daily presents a
major challenge. As a result, documenting psychotherapy by recording and transcribing sessions can provide substantial assistance to therapists by allowing them
to revisit and examine what was said and experienced during the session. However,
transcribing therapy sessions manually is highly time-consuming and requires a lot
of resources. This is why using the current advancements of machine learning to
automatically transcribe and evaluate the therapy sessions seems promising.
While transcription in itself is already very helpful, more advanced analysis tools
that go beyond the literal conversation contents could provide further valuable
insights. In these ways, it could be possible to support the prediction of crucial
therapeutic constructs, such as therapeutic alliance, motivation or symptoms, by
analyzing patterns in speech and language.
},
<br/>   isbn = {978-3-95908-803-9},
<br/>   issn = {0940-6832},
<br/>   keywords = {Poster},
<br/>   url = {https://www.essv.de/pdf/2025_289_296.pdf},
<br/>}