BibTeX

@inProceedings{kokkinakis-etal-2026-disfluencies-364800,
	title        = {Disfluencies and ASR Performance on Swedish Spontaneous Speech from the ‘Trip to Stockholm’ Discourse Narrative Task},
	abstract     = {Automatic Speech Recognition (ASR) offers a scalable and cost-efficient alternative to manual transcription and is becoming increasingly relevant in clinical contexts, particularly for the detection of cognitive decline and mental health assessment. However, current ASR-systems still struggle with spontaneous speech, particularly when processing disfluencies, pauses, and speaker variability that often carry diagnostic value. This study evaluates state-of-the-art open ASR models targeting Swedish using recordings from the “Trip to Stockholm” discourse narrative task which elicits ecologically valid, cognitively demanding speech. Recognition quality is assessed using various metrics, alongside an analysis of linguistic and technical sources of error focused on disfluencies. Our findings show that disfluency-related phenomena degrade recognition performance. Possible post-processing strategies can improve specific error patterns emerging for filled pauses, word repetitions, and self-corrections. The results illustrate both the advances and ongoing limitations of ASR for spontaneous Swedish speech, emphasizing the need for models explicitly trained, or fine-tuned, on disfluent data to ensure robustness in clinical and research applications.},
	booktitle    = {    Proceedings of the Sixth Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments in cooperation with the MENTAL.ai consortium},
	author       = {Kokkinakis, Dimitrios and Lange, Herbert and Muñoz Sánchez, Ricardo},
	year         = {2026},
	publisher    = {European Language Resources Association (ELRA)},
	pages        = {24–33},
}

@inProceedings{francis-etal-2026-chatgpt-364799,
	title        = {ChatGPT, why can’t anyone afford a house? On the Effects of LLM pre-annotation on Annotator Subjectivity},
	abstract     = {Large language models (LLMs) have often been proposed as substitutes for human annotators in a variety of tasks. At the same time, there has been increased focus on the role that human subjectivity and perspective plays in data annotation. To avoid eliminating the human role in annotation entirely, the use of LLMs for pre-annotation has been suggested as an alternative approach. In this paper, we explore to which degree this approach affects subjectivity of social media annotation in English. We focus on comments regarding the current status of the housing market and label them for concern level, factors affecting housing affordability, and aspects that authors claim either exacerbate or improve the situation. To investigate this, we design an experiment involving two rounds of annotation: the first, a dataset annotated by humans only; and the second, a dataset with LLM pre-annotations curated by the same human annotators. We observe that the second setting leads to much higher agreement, as well as significant changes in label distribution and co-occurrence. Similar shifts do not appear in the LLM labels. Our findings show that use of LLMs in the annotation process leads to convergence in annotations and, thus, to an erosion of human subjectivity.},
	booktitle    = {Proceedings of the the fifth edition of NLPerspectives},
	author       = {Francis, Emilie and Leuzinger, Celine and Muñoz Sánchez, Ricardo and Gauthier, Lee D.},
	year         = {2026},
	publisher    = {ELRA Language Resources Association (ELRA)},
	pages        = {98--111},
}

@misc{siegert-etal-2026-proceedings-362841,
	title        = {Proceedings of the Joint Workshop on Legal and Ethical Issues in Human Language Technologies and Computational Approaches to Language Data Pseudonymization, Anonymization, De-identification, and Data Privacy (LEGAL2026 and CALD-pseudo 2026) @ LREC 2026. 11-16 May 2026, Palma, Mallorca, Spain},
	author       = {Siegert, Ingo and Szawerna, Maria Irena and Choukri, Khalid and Dobnik, Simon and Kamocki, Paweł and Lindström Tiedemann, Therese and Lison, Pierre and Muñoz Sánchez, Ricardo and Pilán, Ildikó and Södergård, Lisa and Talmoudi, Kossay and Volodina, Elena and Vu, Xuan-Son},
	year         = {2026},
	publisher    = {ELRA},
	address      = {Paris},
	ISBN         = { 978-2-493814-86-9},
}

@inProceedings{greco-etal-2026-stereobusters-361361,
	title        = {StereoBusters at GSI:detect: LLM-Based Detection and Human Qualitative Analysis of Gender Stereotypes in Italian Short Texts},
	booktitle    = {Proceedings of the 9th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian, Bari, Italy, February 26th-27th, 2026. Final Workshop (EVALITA 2026)},
	author       = {Greco, Salvatore and La Quatra, Moreno and Marchiori Manerba, Marta and Muñoz Sánchez, Ricardo and Cignarella, Alessandra Teresa},
	year         = {2026},
	publisher    = {CEUR Workshop Proceedings},
	address      = {Bari, Italy},
}
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