Zero-shot Emotional Voice Conversion (EVC) aims to transform a speaker’s emotional state to match a target emotion, even for speakers and emotion categories that were not encountered during training, thereby enhancing the generalization ability of traditional EVC systems. Despite advancements in the field, existing methods often face challenges in preserving speaker identity and ensuring the naturalness of emotional expression, particularly in the context of rhythm modeling. To this end, we propose the Zero-Shot Emotion Voice Conversion (ZSEVC) model, which leverages self-supervised learning for speaker adaptation and duration prediction. To adjust speech rhythm in alignment with the target emotional state, we introduce a rhythm-aware content encoder that captures and refines discrete speech units at a finer granularity. Additionally, a hierarchical emotion fusion scheme is employed to integrate emotional features with content features, enhancing both pronunciation accuracy and emotional expressiveness. Moreover, a residual speaker-emotion fusion module is incorporated to better adapt speaker characteristics to emotional prosodic variation. Experimental results show ZSEVC’s superior performance in terms of naturalness and speaker similarity in zero-shot scenario, successfully generating emotional speeches for unseen emotions and speakers. Speech samples are available at https://wosyoo.github.io/ZSEVC.

Emotional 3D talking head generation synthesizes vivid facial expressions with precise lip synchronization for immersive interactions. We introduce DisenEMO, a novel framework designed to disentangle emotion and content from facial motions, thereby facilitating the synthesis of personalized and expressive audio-driven facial animations. To achieve precise emotional disentanglement, we incorporate an intensity perception constraint which improves the accurate perception of categorized emotion and its intensities, leading to the generation of subtle emotional expressions. To ensure the temporal consistency of facial expressions, we introduce facial dynamic modeling, which refines motion trajectories to better capture emotional nuances. Finally, a motion decoder integrates emotional features with audio features extracted from driving speech, producing 3D talking heads with enhanced emotional expressiveness and realism. Experimental results demonstrate that our method outperforms state-of-the-art approaches. Synthesis samples are available at https://c295bw.github.io/DisenEMO-icip25.github.io/.

Major Depressive Disorder (MDD) is a prevalent and severe psychiatric disorder, and its detection remains challenging due to the complexity and variability of its symptoms. Traditional single-modality methods often fail to capture the full spectrum of depressive cues, which has led to the rise of multimodal methods. The ACM Multimedia 2025 ''Multimodal Personality-Aware Depression Detection Challenge'' (MPDD 2025) aims to advance the development of more accurate depression detection models by incorporating multimodal data. In this paper, we proposed a Multi-Level Segment Fusion Based on Adaptive Time-Window Selection (MSF-ATS) method for the MPDD-Elderly Track. To address the challenge of sparse and transient depressive symptoms, we fuse segment-level classifications to obtain subject-level classifications. An adaptive time-window selection based on mean class variance is employed to choose the window with the smallest variance for more stable detection results. Our method achieved an average score of 0.8576 on the MPDD 2025 official test set, significantly outperforming the baseline score of 0.6675.

Asynchronous Video Interviews (AVIs) allow candidates to record responses to predefined questions using digital devices, offering both flexibility and remote accessibility. Assessing personality traits and interview performance via AVIs provides organizations with valuable insights into candidate profiles and facilitates the prediction of future job performance. However, prior benchmark challenges, whose datasets were predominantly sourced from social media, suffer from suboptimal construct and methodological validity, limiting their utility for model development and real-world applications. To address these limitations, we introduce the AVI Grand Challenge at ACM Multimedia 2025, featuring a novel dataset of mock AVIs comprising 3,876 videos from 646 participants in a simulated job application procedure. Interview questions were carefully designed to reflect real-world selection contexts and elicit personality expressions grounded in Trait Activation Theory. Personality traits and job competencies were annotated by trained evaluators and professional recruiters, ensuring both methodological rigor and ecological validity. The solutions and algorithms developed in this challenge are analyzed and summarized in this paper to foster the development of fair, reliable, and AI-driven hiring assessments.

In this paper, we propose Prosody-aware VITS (PAVITS) for emotional voice conversion (EVC), aiming to achieve two major objectives of EVC: high content naturalness and high emotional naturalness, which are crucial for meeting the demands of human perception. To improve the content naturalness of converted audio, we have developed an end-to-end EVC architecture inspired by the high audio quality of VITS. By seamlessly integrating an acoustic converter and vocoder, we effectively address the common issue of mismatch between emotional prosody training and run-time conversion that is prevalent in existing EVC models. To further enhance the emotional naturalness, we introduce an emotion descriptor to model the subtle prosody variations of different speech emotions. Additionally, we propose a prosody predictor, which predicts prosody features from text based on the provided emotion label. Notably, we introduce a prosody alignment loss to establish a connection between latent prosody features from two distinct modalities, ensuring effective training. Experimental results show that the performance of PAVITS is superior to the state-of-the-art EVC methods. Speech Samples are available at https://jeremychee4.github.io/pavits4EVC/.