arXiv:2510. 25577v2 Announce Type: replace-cross Abstract: Recent advances in Speech Foundation Models (SFMs) enable direct processing of raw audio, allowing models to respond to subtle paralinguistic variation.
By Harm Lameris, Shree Harsha Bokkahalli Satish, Joakim Gustafson, \'Eva Sz\'ekely
arXiv:2606. 09589v1 Announce Type: cross Abstract: AI minidramas (also known as fruit dramas) are short, algorithmically distributed generative AI video series featuring anthropomorphized characters that have recently emerged as a widespread phenomenon on social media platforms.
By Piera Riccio
arXiv:2605. 08093v2 Announce Type: replace-cross Abstract: The use of chatbots for various forms of companionship is growing rapidly, raising a myriad of questions about simulated relationships, emotional dependence, and psychological harm.
By Maribeth Rauh, Dick A. H. Blankvoort, Matias Duran, Caoilfhionn N\'i Dheor\'ain, Harshvardhan J. Pandit, Syrine Enneifer, Siddharth D. Jaiswal, Anthony Ventresque, Abeba Birhane
arXiv:2608. 13328v1 Announce Type: cross Abstract: Professional communication is increasingly mediated by LLMs - but do these models serve all users equally?
By Katherine Van Koevering, Anjalie Field
arXiv:2606. 31729v1 Announce Type: cross Abstract: Text-to-speech (TTS) evaluation is an open challenge.
By Dominika Woszczyk, Andreas Triantafyllopoulos, Jura Miniota, \'Eva Sz\'ekely, Bjoern Schuller
Professional communication is increasingly mediated by LLMs - but do these models serve all users equally? We show that when prompts contain linguistic features more commonly used by women (hedges, tag questions, collective reference), they systematically elicit shorter, less sophisticated, and less formal responses across three document types and four models.
arXiv:2606. 07969v1 Announce Type: cross Abstract: Gender bias in AI-generated stories is a well-documented problem.
By Imani Finkley, Yuanxi Li, Melanie Walsh
Large language models (LLMs) are increasingly embedded in everyday life and widely used for information seeking, raising concerns about their potential to perpetuate social biases and reinforce stereotypes. In this study, we investigate gender bias in LLMs through the lens of their associations with musical instruments.
arXiv:2601. 05751v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for everyday communication tasks, including drafting interpersonal messages intended to influence and persuade.
By Amalie Brogaard Pauli, Maria Barrett, Max M\"uller-Eberstein, Isabelle Augenstein, Ira Assent
arXiv:2608. 04433v1 Announce Type: cross Abstract: We present MERaLiON-GR, a speech gender recognition system that performs binary classification (female / male) on English and Southeast Asian (SEA) languages.
By Qiongqiong Wang, Ai Ti Aw, Nancy F. Chen, Ying Lay Chiu, Yang Ding, Yingxu He, Ridong Jiang, Zhuohan Liu, Yanfeng Lu, Yi Ma, Muhammad Huzaifah, Nabilah Binte Md Johan, Nattadaporn Lertcheva, Pham Minh Duc, Sailor Hardik Bhupendra, Siti Umairah Binte Mohammad Salleh, Shuo Sun, Tarun Kumar Vangani, Jeremy H. M. Wong, Jinyang Wu, Longyin Zhang
arXiv:2606. 10911v1 Announce Type: cross Abstract: Claims about the robustness and fairness of deepfake speech detectors are only as credible as the datasets used to train and evaluate those systems.
By Vojt\v{e}ch Stan\v{e}k, Eva Trnovsk\'a, Kamil Malinka, Anton Firc
arXiv:2607. 09891v1 Announce Type: cross Abstract: Audio deepfake detection models determine whether speech is genuine or artificially generated, but high overall accuracy can mask substantial performance disparities across demographic groups.
By Aishwarya R. Fursule, Vamshi Nallaguntla, Shruti Kshirsagar, Anderson R. Avila