Tag: eess.AS
arXiv:2602.22522v1 Announce Type: new Abstract: Taiwanese Hakka is a low-resource, endangered language that poses significant challenges for automatic speech recognition (ASR), including high dialectal variability …
arXiv:2602.23300v1 Announce Type: new Abstract: Emotion Recognition in Conversations (ERC) presents unique challenges, requiring models to capture the temporal flow of multi-turn dialogues and to …
arXiv:2602.20163v1 Announce Type: cross Abstract: Aphasia is an acquired language disorder caused by injury to the regions of the brain that are responsible for language. …
arXiv:2602.20530v1 Announce Type: new Abstract: Emotion recognition from multi-modal physiological and behavioral signals plays a pivotal role in affective computing, yet most existing models remain …
arXiv:2602.15537v1 Announce Type: new Abstract: Pure speech language models aim to learn language directly from raw audio without textual resources. A key challenge is that …
arXiv:2602.17598v1 Announce Type: new Abstract: Current speech LLMs largely perform implicit ASR: on tasks solvable from a transcript, they are behaviorally and mechanistically equivalent to …
arXiv:2602.15909v1 Announce Type: cross Abstract: Deep learning-based respiratory auscultation is currently hindered by two fundamental challenges: (i) inherent information loss, as converting signals into spectrograms …
arXiv:2602.13532v1 Announce Type: new Abstract: In this paper, we propose a fast algorithm for element selection, a multiplication-free form of dimension reduction that produces a …
