@InProceedings{Ehlert2023_1175,
<br/>   author = {Hanna Ehlert and Edith Beaulac and Maren Wallbaum and Christopher Gebauer and Lars Rumberg and Jörn Ostermann and Ulrike Lüdtke},
<br/>   booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2023},
<br/>   title = {Collecting and Annotating Natural Child Speech Data – Challenges and Interdisciplinary Perspectives},
<br/>   year = {2023},
<br/>   editor = {Christoph Draxler},
<br/>   month = mar,
<br/>   pages = {72--78},
<br/>   publisher = {TUDpress, Dresden},
<br/>   abstract  = {In this paper we share experiences on collecting and annotating child
speech data from our speech language therapy background and the TALC-project
(Tools for Analyzing Language and Communication) where we explore the application
of machine learning models (focus ASR) for linguistic and speech therapy
purposes in an interdisciplinary team. We will reflect on the importance of collecting
natural speech data for ASR model training and will summarize recommended
methods for eliciting such spontaneous child speech at different ages. For annotating
recorded data such as transcribing them and marking relevant parts for subsequent
analysis, we will focus on possible ways to ensure communication between
different researchers. Throughout, we will elaborate on the interdisciplinary collaboration
in our project in order to ensure that requirements of model developers
and end-users are met.},
<br/>   isbn = {978-3-95908-303-4},
<br/>   issn = {0940-6832},
<br/>   keywords = {Child Speech},
<br/>   url = {https://www.essv.de/pdf/2023_72_78.pdf},
<br/>}