Zobrazeno 1 - 10
of 86 993
pro vyhledávání: '"Berlin, A. A"'
Autor:
Berlin, Asher, Kahn, Yonatan
The search for dark matter and physics beyond the Standard Model has grown to encompass a highly interdisciplinary approach. In this review, we survey recent searches for light, weakly-coupled particles - axions and dark photons - over the past decad
Externí odkaz:
http://arxiv.org/abs/2412.08704
Millicharged particles are generic in theories of dark sectors. A cosmic or local abundance of them may be produced by the early universe, stellar environments, or the decay or annihilation of dark matter/dark energy. Furthermore, if such particles a
Externí odkaz:
http://arxiv.org/abs/2412.03643
Code-switching-where multilingual speakers alternately switch between languages during conversations-still poses significant challenges to end-to-end (E2E) automatic speech recognition (ASR) systems due to phenomena of both acoustic and semantic conf
Externí odkaz:
http://arxiv.org/abs/2412.08651
Long document summarization poses a significant challenge in natural language processing due to input lengths that exceed the capacity of most state-of-the-art pre-trained language models. This study proposes a hierarchical framework that segments an
Externí odkaz:
http://arxiv.org/abs/2410.06520
Autor:
Wang, Chien-Chun, Chen, Li-Wei, Chou, Cheng-Kang, Lee, Hung-Shin, Chen, Berlin, Wang, Hsin-Min
While pre-trained automatic speech recognition (ASR) systems demonstrate impressive performance on matched domains, their performance often degrades when confronted with channel mismatch stemming from unseen recording environments and conditions. To
Externí odkaz:
http://arxiv.org/abs/2409.12386
Second language (L2) learners can improve their pronunciation by imitating golden speech, especially when the speech that aligns with their respective speech characteristics. This study explores the hypothesis that learner-specific golden speech gene
Externí odkaz:
http://arxiv.org/abs/2409.07151
Automated speaking assessment in conversation tests (ASAC) aims to evaluate the overall speaking proficiency of an L2 (second-language) speaker in a setting where an interlocutor interacts with one or more candidates. Although prior ASAC approaches h
Externí odkaz:
http://arxiv.org/abs/2409.07064
End-to-end (E2E) automatic speech recognition (ASR) models have become standard practice for various commercial applications. However, in real-world scenarios, the long-tailed nature of word distribution often leads E2E ASR models to perform well on
Externí odkaz:
http://arxiv.org/abs/2409.06468
Cross-domain speech enhancement (SE) is often faced with severe challenges due to the scarcity of noise and background information in an unseen target domain, leading to a mismatch between training and test conditions. This study puts forward a novel
Externí odkaz:
http://arxiv.org/abs/2409.01545
The Predicted Mean Vote (PMV) index is a widely accepted method in the building automation sector because it can precisely estimate indoor thermal comfort levels depending on a variety of environmental parameters. This study suggests an experimental
Externí odkaz:
http://arxiv.org/abs/2409.00080