{"id":1163,"date":"2024-12-24T10:48:06","date_gmt":"2024-12-24T09:48:06","guid":{"rendered":"https:\/\/www.cjvt.si\/llm4dh\/?page_id=1163"},"modified":"2025-05-14T12:28:56","modified_gmt":"2025-05-14T10:28:56","slug":"challenge-3","status":"publish","type":"page","link":"https:\/\/www.cjvt.si\/llm4dh\/en\/work-packages\/work-package-3\/","title":{"rendered":"Challenge 3: LLMs for Spoken Language"},"content":{"rendered":"

Challenge 3: LLMs for Spoken Language<\/strong><\/h1>\n<\/div><\/section><\/div>\n

LLMs are fundamentally dependent on data. In language use, substantial variations exist among different types of discourse. The majority of linguistic data derive from written resources. Extensive spoken data resources, if accessible for research, are typically owned by various companies or institutions and are rarely available to the research community. However, even when these data are publicly accessible, they differ significantly from the speech used in everyday conversations. Features such as looser sentence structures, interruptions, cross-talk, silences, repairs, repetitions, misarticulations, clarifications, backchannels, conflicts, slurs, and jargon (Yeomans et al., 2023) exemplify surface-level specifics of conversation and indicate that investigating such data has great impact potential in the linguistic research (Love et al. 2014) as well as poses significant challenges for artificial intelligence (Wahlster 2023). In this challenge, we aim to advance speech technologies and research through the newest methodologies in collecting, filtering, automatically transcribing, and pragmatically processing speech data with the help of LLMs.<\/p>\n<\/div><\/section><\/div>\n<\/div><\/div><\/div><\/div><\/div>

Task 3.1<\/span><\/span><\/span><\/span><\/a>Task 3.2<\/span><\/span><\/span><\/span><\/a>Task 3.3<\/span><\/span><\/span><\/span><\/a>Task 3.4<\/span><\/span><\/span><\/span><\/a>Yearly reports<\/span><\/span><\/span><\/span><\/a><\/div>
<\/span><\/span>\n

T<\/em><\/strong>3.1 Efficient spoken language data collection <\/em><\/strong><\/h3>\n<\/div><\/section>
\n
\n
\n

Some conversational data in Slovene are available (Verdonik et al. 2024), yet they fall significantly short of the current state-of-the-art (Love et al. 2017). Thus, developing a modern and sustainable solution for conversational data collection is the first challenge we address. Collecting speech data remotely is essential for obtaining geographically dispersed data, especially for languages other than English (Parent & Esk\u00e9nazi, 2011). However, the primary challenge lies in motivating citizens to donate speech. Little attention has been devoted to comprehending citizens\u2019 motivation for contributing speech data or to citizens\u2019 attitudes toward technologies based on LLMs. We aim to investigate citizens\u2019 perspectives on speech-enabled LLM technologies, identify their motivations, and establish an effective online approach for sustainable speech data acquisition.<\/p>\n<\/div>\n

We will use successful crowdsourcing platforms, Games-With-A-Purpose (GWAPs) and Collect4NLP, designing an online interface for collecting conversational speech data, taking into account economic, ethical, and legal aspects, while simplifying and automating metadata collection. Further, we will investigate user perspectives on speech-enabled LLM technologies and explore strategies to motivate speakers to contribute to speech databases, e.g., the importance of trust and privacy. We will apply self-determination theory to highlight the role of intrinsic motivations like contributing to scientific research, preserving language heritage, and social connection. The collected audio data, manually transcribed data, and the multi-reference transcription of the benchmarking part of the data will be publicly released.<\/p>\n

<\/div>\n<\/section>\n
\n
<\/div>\n<\/section>\n<\/div><\/section>
\n