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pro vyhledávání: '"Homayounpour, Mohammad Mehdi"'
Publikováno v:
In Neurocomputing 7 February 2025 617
Deep neural network models are used today in various applications of artificial intelligence, the strengthening of which, in the face of adversarial attacks is of particular importance. An appropriate solution to adversarial attacks is adversarial tr
Externí odkaz:
http://arxiv.org/abs/2202.02626
Many endeavors have sought to develop countermeasure techniques as enhancements on Automatic Speaker Verification (ASV) systems, in order to make them more robust against spoof attacks. As evidenced by the latest ASVspoof 2019 countermeasure challeng
Externí odkaz:
http://arxiv.org/abs/2109.02051
This paper proposes a new method for calculating joint-state posteriors of mixed-audio features using deep neural networks to be used in factorial speech processing models. The joint-state posterior information is required in factorial models to perf
Externí odkaz:
http://arxiv.org/abs/1707.02661
Neural Machine Translation (NMT) is a new approach for Machine Translation (MT), and due to its success, it has absorbed the attention of many researchers in the field. In this paper, we study NMT model on Persian-English language pairs, to analyze t
Externí odkaz:
http://arxiv.org/abs/1701.01854
A Pascal challenge entitled monaural multi-talker speech recognition was developed, targeting the problem of robust automatic speech recognition against speech like noises which significantly degrades the performance of automatic speech recognition s
Externí odkaz:
http://arxiv.org/abs/1610.01367
This paper investigates the effectiveness of factorial speech processing models in noise-robust automatic speech recognition tasks. For this purpose, the paper proposes an idealistic approach for modeling state-conditional observation distribution of
Externí odkaz:
http://arxiv.org/abs/1503.02578
Publikováno v:
In Expert Systems With Applications 30 December 2020 162
Autor:
Kaabinejadian, Amirreza, Maghsoudi, Peyman, Homayounpour, Mohammad Mehdi, Sadeghi, Sadegh, Bidabadi, Mehdi, Xu, Fei
Publikováno v:
In Renewable Energy December 2020 162:1618-1628
Publikováno v:
Int. J. Patt. Recogn. Artif. Intell. 29, 1551006 (2015)
Nowadays this is very popular to use deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. In this p
Externí odkaz:
http://arxiv.org/abs/1411.4046