Zobrazeno 1 - 10
of 6 514
pro vyhledávání: '"A. Povey"'
Publikováno v:
Atmospheric Measurement Techniques, Vol 17, Pp 2521-2538 (2024)
Atmospheric aerosols have pronounced effects on climate at both regional and global scales, but the magnitude of these effects is subject to considerable uncertainties. A major contributor to these uncertainties is an incomplete understanding of the
Externí odkaz:
https://doaj.org/article/baaa54d6f78a438d9913872c2eebb75d
Autor:
Povey, Anna, Povey, Katherine
This paper introduces FeruzaSpeech, a read speech corpus of the Uzbek language, containing transcripts in both Cyrillic and Latin alphabets, freely available for academic research purposes. This corpus includes 60 hours of high-quality recordings fro
Externí odkaz:
http://arxiv.org/abs/2410.00035
Autor:
M. W. Christensen, A. Gettelman, J. Cermak, G. Dagan, M. Diamond, A. Douglas, G. Feingold, F. Glassmeier, T. Goren, D. P. Grosvenor, E. Gryspeerdt, R. Kahn, Z. Li, P.-L. Ma, F. Malavelle, I. L. McCoy, D. T. McCoy, G. McFarquhar, J. Mülmenstädt, S. Pal, A. Possner, A. Povey, J. Quaas, D. Rosenfeld, A. Schmidt, R. Schrödner, A. Sorooshian, P. Stier, V. Toll, D. Watson-Parris, R. Wood, M. Yang, T. Yuan
Publikováno v:
Atmospheric Chemistry and Physics, Vol 22, Pp 641-674 (2022)
Aerosol–cloud interactions (ACIs) are considered to be the most uncertain driver of present-day radiative forcing due to human activities. The nonlinearity of cloud-state changes to aerosol perturbations make it challenging to attribute causality i
Externí odkaz:
https://doaj.org/article/b67802aa319b4f9ea1ad4a07bfa66e71
Autor:
Yao, Zengwei, Kang, Wei, Yang, Xiaoyu, Kuang, Fangjun, Guo, Liyong, Zhu, Han, Jin, Zengrui, Li, Zhaoqing, Lin, Long, Povey, Daniel
Connectionist Temporal Classification (CTC) is a widely used method for automatic speech recognition (ASR), renowned for its simplicity and computational efficiency. However, it often falls short in recognition performance compared to transducer or s
Externí odkaz:
http://arxiv.org/abs/2410.05101
Autor:
Jin, Zengrui, Yang, Yifan, Shi, Mohan, Kang, Wei, Yang, Xiaoyu, Yao, Zengwei, Kuang, Fangjun, Guo, Liyong, Meng, Lingwei, Lin, Long, Xu, Yong, Zhang, Shi-Xiong, Povey, Daniel
The evolving speech processing landscape is increasingly focused on complex scenarios like meetings or cocktail parties with multiple simultaneous speakers and far-field conditions. Existing methodologies for addressing these challenges fall into two
Externí odkaz:
http://arxiv.org/abs/2409.00819
Autor:
Wu, Xuntao, Yan, Haoxiong, Andersson, Gustav, Anferov, Alexander, Chou, Ming-Han, Conner, Christopher R., Grebel, Joel, Joshi, Yash J., Li, Shiheng, Miller, Jacob M., Povey, Rhys G., Qiao, Hong, Cleland, Andrew N.
Superconducting qubits provide a promising approach to large-scale fault-tolerant quantum computing. However, qubit connectivity on a planar surface is typically restricted to only a few neighboring qubits. Achieving longer-range and more flexible co
Externí odkaz:
http://arxiv.org/abs/2407.20134
Existing research suggests that automatic speech recognition (ASR) models can benefit from additional contexts (e.g., contact lists, user specified vocabulary). Rare words and named entities can be better recognized with contexts. In this work, we pr
Externí odkaz:
http://arxiv.org/abs/2407.10303
Autor:
Shao, Yiwen, Zhang, Shi-Xiong, Xu, Yong, Yu, Meng, Yu, Dong, Povey, Daniel, Khudanpur, Sanjeev
In the field of multi-channel, multi-speaker Automatic Speech Recognition (ASR), the task of discerning and accurately transcribing a target speaker's speech within background noise remains a formidable challenge. Traditional approaches often rely on
Externí odkaz:
http://arxiv.org/abs/2406.09589
Autor:
Wang, Quandong, Yuan, Yuxuan, Yang, Xiaoyu, Zhang, Ruike, Zhao, Kang, Liu, Wei, Luan, Jian, Povey, Daniel, Wang, Bin
While Large Language Models (LLMs) have achieved remarkable success in various fields, the efficiency of training and inference remains a major challenge. To address this issue, we propose SUBLLM, short for Subsampling-Upsampling-Bypass Large Languag
Externí odkaz:
http://arxiv.org/abs/2406.06571
Autor:
Huang, Ruizhe, Zhang, Xiaohui, Ni, Zhaoheng, Sun, Li, Hira, Moto, Hwang, Jeff, Manohar, Vimal, Pratap, Vineel, Wiesner, Matthew, Watanabe, Shinji, Povey, Daniel, Khudanpur, Sanjeev
Connectionist temporal classification (CTC) models are known to have peaky output distributions. Such behavior is not a problem for automatic speech recognition (ASR), but it can cause inaccurate forced alignments (FA), especially at finer granularit
Externí odkaz:
http://arxiv.org/abs/2406.02560