GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations Y upei Li, Qiyang Sun, Sunil Munthumoduku Krishna Murthy, Emran Alturki, and Bj orn W . Schuller Fellow, IEEE Abstract --Affective Computing (AC) is essential for advancing Artificial General Intelligence (AGI), with emotion recognition serving as a key component. However, human emotions are inherently dynamic, influenced not only by an individual's expressions but also by interactions with others, and single-modality approaches often fail to capture their full dynamics. Multimodal Emotion Recognition (MER) leverages multiple signals but traditionally relies on utterance-level analysis, overlooking the dynamic nature of emotions in conversations. Emotion Recognition in Conversation (ERC) addresses this limitation, yet existing methods struggle to align multimodal features and explain why emotions evolve within dialogues. T o bridge this gap, we propose GatedxLSTM, a novel speech-text multimodal ERC model that explicitly considers voice and transcripts of both the speaker and their conversational partner(s) to identify the most influential sentences driving emotional shifts. By integrating Contrastive Language-Audio Pretraining (CLAP) for improved cross-modal alignment and employing a gating mechanism to emphasise emotionally impactful utterances, GatedxLSTM enhances both interpretability and performance. Experiments on the IEMOCAP dataset demonstrate that GatedxLSTM achieves state-of-the-art (SOT A) performance among open-source methods in four-class emotion classification. These results validate its effectiveness for ERC applications and provide an interpretability analysis from a psychological perspective. I NTRODUCTION Artificial General Intelligence (AGI) represents a key future direction in AI development, with Affective Computing (AC) playing a crucial role in enhancing AGI's ability to interact effectively with humans. Sunil Munthumoduku Krishna Murthy is with CHI - Chair of Health Informatics, MRI, Technical University of Munich, Germany (e-mail: sunil.munthumoduku@tum.de). Bj orn W . Schuller is with GLAM, Department of Computing, Imperial College London, UK; CHI - Chair of Health Informatics, Technical University of Munich, Germany; relAI - the Konrad Zuse School of Excellence in Reliable AI, Munich, Germany; MDSI - Munich Data Science Institute, Munich, Germany; and MCML - Munich Center for Machine Learning, Munich, Germany (e-mail: schuller@tum.de). Y upei Li and Qiyang Sun contributed equally to this work.

As Artificial Intelligence (AI) continues to advance rapidly, Friendly AI (FAI) has been proposed to advocate for more equitable and fair development of AI. Despite its importance, there is a lack of comprehensive reviews examining FAI from an ethical perspective, as well as limited discussion on its potential applications and future directions. This paper addresses these gaps by providing a thorough review of FAI, focusing on theoretical perspectives both for and against its development, and presenting a formal definition in a clear and accessible format. Key applications are discussed from the perspectives of eXplainable AI (XAI), privacy, fairness and affective computing (AC). Additionally, the paper identifies challenges in current technological advancements and explores future research avenues. The findings emphasise the significance of developing FAI and advocate for its continued advancement to ensure ethical and beneficial AI development.

The continuous development of artificial intelligence (AI) theory has propelled this field to unprecedented heights, owing to the relentless efforts of scholars and researchers. In the medical realm, AI takes a pivotal role, leveraging robust machine learning (ML) algorithms. AI technology in medical imaging aids physicians in X-ray, computed tomography (CT) scans, and magnetic resonance imaging (MRI) diagnoses, conducts pattern recognition and disease prediction based on acoustic data, delivers prognoses on disease types and developmental trends for patients, and employs intelligent health management wearable devices with human-computer interaction technology to name but a few. While these well-established applications have significantly assisted in medical field diagnoses, clinical decision-making, and management, collaboration between the medical and AI sectors faces an urgent challenge: How to substantiate the reliability of decision-making? The underlying issue stems from the conflict between the demand for accountability and result transparency in medical scenarios and the black-box model traits of AI. This article reviews recent research grounded in explainable artificial intelligence (XAI), with an emphasis on medical practices within the visual, audio, and multimodal perspectives. We endeavour to categorise and synthesise these practices, aiming to provide support and guidance for future researchers and healthcare professionals.

Audio-based kinship verification (AKV) is important in many domains, such as home security monitoring, forensic identification, and social network analysis. A key challenge in the task arises from differences in age across samples from different individuals, which can be interpreted as a domain bias in a cross-domain verification task. To address this issue, we design the notion of an "age-standardised domain" wherein we utilise the optimised CycleGAN-VC3 network to perform age-audio conversion to generate the in-domain audio. The generated audio dataset is employed to extract a range of features, which are then fed into a metric learning architecture to verify kinship. Experiments are conducted on the KAN_AV audio dataset, which contains age and kinship labels. The results demonstrate that the method markedly enhances the accuracy of kinship verification, while also offering novel insights for future kinship verification research.

The increasing success of audio foundation models across various tasks has led to a growing need for improved interpretability to understand their intricate decision-making processes better. Existing methods primarily focus on explaining these models by attributing importance to elements within the input space based on their influence on the final decision. In this paper, we introduce a novel audio explanation method that capitalises on the generative capacity of audio foundation models. Our method leverages the intrinsic representational power of the embedding space within these models by integrating established feature attribution techniques to identify significant features in this space. The method then generates listenable audio explanations by prioritising the most important features. Through rigorous benchmarking against standard datasets, including keyword spotting and speech emotion recognition, our model demonstrates its efficacy in producing audio explanations.