arXiv:2508.01277v2 Announce Type: replace-cross
Abstract: Automated bioacoustic analysis is essential for biodiversity monitoring and conservation, requiring advanced deep learning models that can adapt to diverse bioacoustic tasks. This article prese…
arXiv:2603.27001v1 Announce Type: cross
Abstract: Speaker anonymization (SA) systems modify timbre while leaving regional or non-native accents intact, which is problematic because accents can narrow the anonymity set. To address this issue, we presen…
arXiv:2603.27237v1 Announce Type: cross
Abstract: This study explores the extent to which deep learning models can predict groove and its related perceptual dimensions directly from audio signals. We critically examine the effectiveness of seven state…
arXiv:2603.28737v1 Announce Type: cross
Abstract: We introduce ParaSpeechCLAP, a dual-encoder contrastive model that maps speech and text style captions into a common embedding space, supporting a wide range of intrinsic (speaker-level) and situationa…
arXiv:2510.06961v4 Announce Type: replace
Abstract: We present the Open ASR Leaderboard, a reproducible benchmarking platform with community contributions from academia and industry. It compares 86 open-source and proprietary systems across 12 dataset…
arXiv:2603.26939v1 Announce Type: cross
Abstract: This paper presents a multi-label stuttering detection system trained on multi-corpus, multilingual data in English, German, and Mandarin.By leveraging annotated stuttering data from three languages an…
arXiv:2603.24596v2 Announce Type: replace-cross
Abstract: While the shift from cascaded dialogue systems to end-to-end (E2E) speech Large Language Models (LLMs) improves latency and paralinguistic modeling, E2E models often exhibit a significant perfo…
arXiv:2603.26795v1 Announce Type: cross
Abstract: Building a diagnosis model for primary progressive aphasia (PPA) has been challenging due to the data scarcity. Collecting clinical data at scale is limited by the high vulnerability of clinical popula…
arXiv:2603.26840v1 Announce Type: cross
Abstract: Multimodal Emotion Recognition in Conversations (MERC) aims to predict speakers’ emotional states in multi-turn dialogues through text, audio, and visual cues. In real-world settings, conversation scen…
arXiv:2603.26856v1 Announce Type: cross
Abstract: The rapid advancement of generative models has enabled highly realistic audio deepfakes, yet current detectors suffer from a critical bias problem, leading to poor generalization across unseen datasets…