7 citations · 14 across the 6 of their papers we have counts for
6 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…
ArmanTTS single-speaker Persian dataset
Mohammd Hasan Shamgholi, Vahid Saeedi, Javad Peymanfard +2
TTS, or text-to-speech, is a complicated process that can be accomplished through appropriate modeling using deep learning methods. In order to implement deep learning models, a su…
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…
ArmanEmo: A Persian Dataset for Text-based Emotion Detection
Hossein Mirzaee, Javad Peymanfard, Hamid Habibzadeh Moshtaghin +1
With the recent proliferation of open textual data on social media platforms, Emotion Detection (ED) from Text has received more attention over the past years. It has many applicat…
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…