26 citations · 34 across the 8 of their papers we have counts for
8 papers
Leveraging Visemes for Better Visual Speech Representation and Lip Reading
Javad Peymanfard, Vahid Saeedi, Mohammad Reza Mohammadi +2
Lip reading is a challenging task that has many potential applications in speech recognition, human-computer interaction, and security systems. However, existing lip reading system…
Word-level Persian Lipreading Dataset
Javad Peymanfard, Ali Lashini, Samin Heydarian +2
Lip-reading has made impressive progress in recent years, driven by advances in deep learning. Nonetheless, the prerequisite such advances is a suitable dataset. This paper provide…
A Multi-Purpose Audio-Visual Corpus for Multi-Modal Persian Speech Recognition: the Arman-AV Dataset
Javad Peymanfard, Samin Heydarian, Ali Lashini +3
In recent years, significant progress has been made in automatic lip reading. But these methods require large-scale datasets that do not exist for many low-resource languages. In t…
MAS2HP: A Multi Agent System to Predict Protein Structure in 2D HP model
Hossein Parineh, Nasser Mozayani
Protein Structure Prediction (PSP) is an unsolved problem in the field of computational biology. The problem of protein structure prediction is about predicting the native conforma…
Smoking prevalence in Covid-19 patients
Sina Bagheri-Nezhad, Nasser Mozayani, Elham Abdi +1
We investigate the prevalence rate of smoking in Covid-19 patients and examine whether there is a difference in the distribution of smokers between the two statistical populations…
Lip reading using external viseme decoding
Javad Peymanfard, Mohammad Reza Mohammadi, Hossein Zeinali +1
Lip-reading is the operation of recognizing speech from lip movements. This is a difficult task because the movements of the lips when pronouncing the words are similar for some of…
A new Potential-Based Reward Shaping for Reinforcement Learning Agent
Babak Badnava, Mona Esmaeili, Nasser Mozayani +1
Potential-based reward shaping (PBRS) is a particular category of machine learning methods which aims to improve the learning speed of a reinforcement learning agent by extracting…
Learning to predict where to look in interactive environments using deep recurrent q-learning
Sajad Mousavi, Michael Schukat, Enda Howley +2
Bottom-Up (BU) saliency models do not perform well in complex interactive environments where humans are actively engaged in tasks (e.g., sandwich making and playing the video games…