Zobrazeno 1 - 10
of 17 559
pro vyhledávání: '"Wang, An Min"'
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
Autor:
Ren, Wenze, Wu, Haibin, Lin, Yi-Cheng, Chen, Xuanjun, Chao, Rong, Hung, Kuo-Hsuan, Li, You-Jin, Ting, Wen-Yuan, Wang, Hsin-Min, Tsao, Yu
In multichannel speech enhancement, effectively capturing spatial and spectral information across different microphones is crucial for noise reduction. Traditional methods, such as CNN or LSTM, attempt to model the temporal dynamics of full-band and
Externí odkaz:
http://arxiv.org/abs/2409.10376
This work investigates two strategies for zero-shot non-intrusive speech assessment leveraging large language models. First, we explore the audio analysis capabilities of GPT-4o. Second, we propose GPT-Whisper, which uses Whisper as an audio-to-text
Externí odkaz:
http://arxiv.org/abs/2409.09914
Autor:
Wang, Ning, Kang, Jia-Min, Lu, Wen-Long, Wang, Shao-Min, Wang, You-Jia, Li, Hai-Ou, Cao, Gang, Wang, Bao-Chuan, Guo, Guo-Ping
Scaling up quantum dots to two-dimensional (2D) arrays is a crucial step for advancing semiconductor quantum computation. However, maintaining excellent tunability of quantum dot parameters, including both nearest-neighbor and next-nearest-neighbor c
Externí odkaz:
http://arxiv.org/abs/2409.09761
Autor:
Wang, Ning, Wang, Shao-Min, Zhang, Run-Ze, Kang, Jia-Min, Lu, Wen-Long, Li, Hai-Ou, Cao, Gang, Wang, Bao-Chuan, Guo, Guo-Ping
Electron spin qubits in silicon are a promising platform for fault-tolerant quantum computing. Low-frequency noise, including nuclear spin fluctuations and charge noise, is a primary factor limiting gate fidelities. Suppressing this noise is crucial
Externí odkaz:
http://arxiv.org/abs/2409.09747
This study investigates the efficacy of data augmentation techniques for low-resource automatic speech recognition (ASR), focusing on two endangered Austronesian languages, Amis and Seediq. Recognizing the potential of self-supervised learning (SSL)
Externí odkaz:
http://arxiv.org/abs/2409.08872
Autor:
Huang, Wen-Chin, Fu, Szu-Wei, Cooper, Erica, Zezario, Ryandhimas E., Toda, Tomoki, Wang, Hsin-Min, Yamagishi, Junichi, Tsao, Yu
We present the third edition of the VoiceMOS Challenge, a scientific initiative designed to advance research into automatic prediction of human speech ratings. There were three tracks. The first track was on predicting the quality of ``zoomed-in'' hi
Externí odkaz:
http://arxiv.org/abs/2409.07001
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
Autor:
Yao, Zhu-Heng, Yang, Sen, Guo, Wei-Jian, Chen, Yong-Jie, Songsheng, Yu-Yang, Bao, Dong-Wei, Jiang, Bo-Wei, Wang, Yi-Lin, Zhang, Hao, Hu, Chen, Li, Yan-Rong, Du, Pu, Xiao, Ming, Bai, Jin-Ming, Ho, Luis C., Brotherton, Michael S., Aceituno, Jesús, Winkler, Hartmut, Wang, Jian-Min
Over the past three decades, multiple reverberation mapping (RM) campaigns conducted for the quasar PG 2130+099 have exhibited inconsistent findings with time delays ranging from $\sim$10 to $\sim$200 days. To achieve a comprehensive understanding of
Externí odkaz:
http://arxiv.org/abs/2408.17407
Relativistic polarized electron beams can find applications in broad areas of fundamental physics. Here, we propose for the first time that electron spin polarization can be realized efficiently via collective beam-target interactions. When a relativ
Externí odkaz:
http://arxiv.org/abs/2408.05768