In this paper we present our proposed systems, for Tasks 1 and 5 of the #SMM4H-2024 shared task (Social Media Mining for Health), responsible for identifying health-related aspects in English social media text. Task 1 consisted of identifying text spans mentioning adverse drug events and linking them to unique identifiers from the medical terminology MedDRA, whereas in Task 5 the aim was to distinguish tweets that report a user having a child with a medical disorder from tweets that merely mention a disorder.For Task 1, our system, composed of a pre-trained RoBERTa model and a random forest classifier, achieved 0.397 and 0.295 entity recognition and normalization F1-scores respectively. In Task 5, we obtained a 0.840 F1-score using a pre-trained BERT model.

This paper describes BioInfo@UAVR team’s approach for adressing subtasks 1a and 1b of the Social Media Mining for Health Applications 2022 shared task. These sub-tasks deal with the classification of tweets that contain an Adverse Drug Event mentions and the detection of spans that correspond to those mentions. Our approach relies on transformer-based models, data augmentation, and an external dataset.

In this paper we share our experience and describe the methodologies that we have used in designing and recording large speech databases for applications requiring speech synthesis. Given the growing demand for customized and domain specific voices for use in corpus based synthesis systems, we believe that good practices should be established for the creation of these databases which are a key factor in the quality of the resulting speech synthesizer. We will focus on the designing of the recording prompts, on the speaker selection procedure, on the recording setup and on the quality control of the resulting database. One of the major challenges was to assure the uniformity of the recordings during the 20 two-hour recording sessions that each speaker had to perform, to produce a total of 13 hours of recorded speech for each of the four speakers. This work was conducted in the scope of the Tecnovoz project that brought together 4 speech research centers and 9 companies with the goal of integrating speech technologies in a wide range of applications.