The present benchmarks for testing the audio modality of multimodal large language models concentrate on testing various audio tasks such as speaker diarization or gender identification in isolation. Whether a multimodal model can answer the questions that require reasoning skills to combine audio tasks of different categories cannot be verified with their use. To address this issue, we propose Audio Reasoning Tasks (ART), a new benchmark for assessing the ability of multimodal models to solve problems that require reasoning over audio signal.

This paper introduces the Polish Speech Emotion Recognition Challenge, a shared task aimed at advancing research on cross-lingual emotion recognition in low-resource languages. The challenge’s objective was to develop systems that could recognize emotional states in Polish speech using only multilingual training data, with no access to Polish training examples. The final test set consisted of newly recorded Polish speech samples created specifically for the challenge, ensuring a fully blind evaluation. Participants submitted emotion predictions for six target classes. System performance was assessed using the macro-averaged F1 score as the primary metric.

Speech emotion recognition has become increasingly important in recent years due to its potential applications in healthcare, customer service, and personalization of dialogue systems. However, a major issue in this field is the lack of datasets that adequately represent basic emotional states across various language families. As datasets covering Slavic languages are rare, there is a need to address this research gap. This paper presents the development of nEMO, a novel corpus of emotional speech in Polish. The dataset comprises over 3 hours of samples recorded with the participation of nine actors portraying six emotional states: anger, fear, happiness, sadness, surprise, and a neutral state. The text material used was carefully selected to represent the phonetics of the Polish language adequately. The corpus is freely available under the terms of a Creative Commons license (CC BY-NC-SA 4.0).

This position paper is an overview of author’s main research interests and work considering deep learning techniques in audio classification, sign languages, and multimodality in dialogue systems. Author also shares her opinion on current and future research considering dialogue agents, and suggests topics for discussion panels.