TY - CHAP A1 - Frohnmaier, Mariano A1 - Freisinger, Steffen A1 - Holt, Madeline Faye A1 - Georges, Munir ED - Baumann, Timo T1 - NoiSLU: a Noisy speech corpus for Spoken Language Understanding in the Public Transport Domain T2 - Elektronische Sprachsignalverarbeitung 2024, Tagungsband der 35. Konferenz, Regensburg, 6.-8. März 2024 N2 - The use of local public transport requires the barrier-free purchase of a ticket. Travellers who are not proficient in the local language benefit from a multilingual human(ticket)machine voice interaction. This paper presents a nearly parallel audio dataset with 13218 annotated user queries from 20 speakers for English, German and Dutch. The domain-specific speech corpus can be understood as an evaluation dataset for future research in Spoken Language Understanding (SLU) and thus, it enables researches to improve the quality of human-machine interaction applications. Furthermore, we compare the SLU performance of different compositions of Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) models in baseline experiments on different test datasets. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-70848 SN - 978-3-95908-325-6 SP - 86 EP - 93 PB - TUDpress CY - Dresden ER - TY - CHAP A1 - Ranzenberger, Thomas A1 - Bocklet, Tobias A1 - Freisinger, Steffen A1 - Georges, Munir A1 - Glocker, Kevin A1 - Herygers, Aaricia A1 - Riedhammer, Korbinian A1 - Schneider, Fabian A1 - Simic, Christopher A1 - Zakaria, Khabbab ED - Baumann, Timo T1 - Extending HAnS: Large Language Models For Question Answering, Summarization, And Topic Segmentation In An ML-based Learning Experience Platform T2 - Elektronische Sprachsignalverarbeitung 2024, Tagungsband der 35. Konferenz, Regensburg, 6.-8. März 2024 N2 - The use of chatbots based on large language models (LLMs) and their impact on society are influencing our learning experience platform Hochschul-Assistenz-System (HAnS). HAnS uses machine learning (ML) methods to support students and lecturers in the online learning and teaching processes [1]. This paper introduces LLM-based features available in HAnS which are using the transcript of our improved Automatic Speech Recognition (ASR) pipeline with an average transcription duration of 45 seconds and an average word error rate (WER) of 6.66% on over 8 hours of audio data of 7 lecture videos. A LLM-based chatbot could be used to answer questions on the lecture content as the ASR transcript is provided as context. The summarization and topic segmentation uses the LLM to improve our learning experience platform. We generate multiple choice questions using the LLM and the ASR transcript as context during playback in a period of 3 minutes and display them in the HAnS frontend. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-71032 SN - 978-3-95908-325-6 SP - 219 EP - 224 PB - TUDpress CY - Dresden ER -