Isabel Trancoso

Offense and hate speech are a source of online conflicts which have become common in social media and, as such, their study is a growing topic of research in machine learning and natural language processing. This article presents two Portuguese language offense-related datasets that deepen the study of the subject: an Aggressiveness dataset and a Conflicts/Attacks dataset. While the former is similar to other offense detection related datasets, the latter constitutes a novelty due to the use of the history of the interaction between users. Several studies were carried out to construct and analyze the data in the datasets. The first study included gathering expressions of verbal aggression witnessed by adolescents to guide data extraction for the datasets. The second study included extracting data from Twitter (in Portuguese) that matched the most frequent expressions/words/sentences that were identified in the previous study. The third study consisted in the development of the Aggressiveness dataset, the Conflicts/Attacks dataset, and classification models. In our fourth study, we proposed to examine whether online aggression and conflicts/attacks revealed any trend changes over time with a sample of 86 adolescents. With this study, we also proposed to investigate whether the amount of tweets sent over a period of 273 days was related to online aggression and conflicts/attacks. Lastly, we analyzed the percentage of participants who participated in the aggressions and/or attacks/conflicts.

Speech is a rich biomarker that encodes substantial information about the health of a speaker, and thus it has been proposed for the detection of numerous diseases, achieving promising results. However, questions remain about what the models trained for the automatic detection of these diseases are actually learning and the basis for their predictions, which can significantly impact patients’ lives. This work advocates for an interpretable health model, suitable for detecting several diseases, motivated by the observation that speech-affecting disorders often have overlapping effects on speech signals. A framework is presented that first defines "reference speech" and then leverages this definition for disease detection. Reference speech is characterized through reference intervals, i.e., the typical values of clinically meaningful acoustic and linguistic features derived from a reference population. This novel approach in the field of speech as a biomarker is inspired by the use of reference intervals in clinical laboratory science. Deviations of new speakers from this reference model are quantified and used as input to detect Alzheimer’s and Parkinson’s disease. The classification strategy explored is based on Neural Additive Models, a type of glass-box neural network, which enables interpretability. The proposed framework for reference speech characterization and disease detection is designed to support the medical community by providing clinically meaningful explanations that can serve as a valuable second opinion.

Speech is a rich biomarker that encodes substantial information about the health of a speaker, and thus it has been proposed for the detection of numerous diseases, achieving promising results. However, questions remain about what the models trained for the automatic detection of these diseases are actually learning and the basis for their predictions, which can significantly impact patients' lives. This work advocates for an interpretable health model, suitable for detecting several diseases, motivated by the observation that speech-affecting disorders often have overlapping effects on speech signals. A framework is presented that first defines "reference speech" and then leverages this definition for disease detection. Reference speech is characterized through reference intervals, i.e., the typical values of clinically meaningful acoustic and linguistic features derived from a reference population. This novel approach in the field of speech as a biomarker is inspired by the use of reference intervals in clinical laboratory science. Deviations of new speakers from this reference model are quantified and used as input to detect Alzheimer's and Parkinson's disease. The classification strategy explored is based on Neural Additive Models, a type of glass-box neural network, which enables interpretability. The proposed framework for reference speech characterization and disease detection is designed to support the medical community by providing clinically meaningful explanations that can serve as a valuable second opinion.

John Mendonça, Isabel Trancoso, Alon Lavie. Findings of the Association for Computational Linguistics: EMNLP 2024. 2024.

John Mendonca, Isabel Trancoso, Alon Lavie. Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2024.

John Mendonça, Alon Lavie, Isabel Trancoso. Proceedings of the 6th Workshop on NLP for Conversational AI (NLP4ConvAI 2024). 2024.

In the current digital era, language technologies are playing an increasingly vital role in the legal domain, assisting users, lawyers, judges, and legal professionals to solve many real-world problems. While open datasets and innovative deep learning methodologies have led to recent breakthroughs in the area, significant efforts are still being made to transfer the theoretical/algorithmic developments, associated with general text and speech processing, into real applications in the legal-domain. This chapter presents a brief survey on language technologies for addressing legal tasks, covering studies and applications related to both text and speech processing (Manuscript submitted in May 2022).

Automatic Speaker Diarization (ASD) is an enabling technology with numerous applications, which deals with recordings of multiple speakers, raising special concerns in terms of privacy. In fact, in remote settings, where recordings are shared with a server, clients relinquish not only the privacy of their conversation, but also of all the information that can be inferred from their voices. However, to the best of our knowledge, the development of privacy-preserving ASD systems has been overlooked thus far. In this work, we tackle this problem using a combination of two cryptographic techniques, Secure Multiparty Computation (SMC) and Secure Modular Hashing, and apply them to the two main steps of a cascaded ASD system: speaker embedding extraction and agglomerative hierarchical clustering. Our system is able to achieve a reasonable trade-off between performance and efficiency, presenting real-time factors of 1.1 and 1.6, for two different SMC security settings.