TY - CONF A1 - Mühlhausen, Sara A1 - Gomez, Sarah A1 - Lauer, Norina A1 - Baumann, Timo A2 - Grawunder, Sven T1 - Cross lingual transfer learning does not improve aphasic speech recognition T2 - Elektronische Sprachsignalverarbeitung 2025: Tagungsband der 36. Konferenz Halle/Saale, 05.–07. MÄRZ 2025 N2 - In addressing the particular linguistic challenges posed by patients suffering from aphasia, a language disorder, this paper proposes a fine-tuning approach to enhance the speech recognition capabilities of existing models. The available aphasic research data in German is highly limited. To address this constraint, we propose a cross-lingual transfer approach to utilize English data to improve performance in German. This advancement aims to support the development of a therapy platform tailored for patients with aphasia. For the base speech recognition model, we choose to use OpenAI’s Whisper model, and for fine-tuning, we make use of TalkBank’s AphasiaBank. The experimental findings demonstrate that the transcription of aphasic audio with Whisper is less successful than non-aphasic audio. However, fine-tuning the transcription in the respective language resulted in an enhancement of its quality. In contrast, fine-tuning the transcription in another language and expecting a transfer of the learned aphasic speech properties led to a deterioration in its quality. KW - Automatische Spracherkennung KW - Sprachdialogsystem KW - Aphasie Y1 - 2025 UR - https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/8051 UR - https://nbn-resolving.org/urn:nbn:de:bvb:898-opus4-80518 UR - https://www.essv.de/pdf/2025_77_84.pdf SN - 978-3-95908-803-9 SN - 0940-6832 PB - TUDpress CY - Dresden ER -