M
Mehmet Ali Kutlugun
Researcher at Istanbul Sabahattin Zaim University
Publications - 6
Citations - 21
Mehmet Ali Kutlugun is an academic researcher from Istanbul Sabahattin Zaim University. The author has contributed to research in topics: Trigram & Speech synthesis. The author has an hindex of 2, co-authored 5 publications receiving 12 citations.
Papers
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Proceedings ArticleDOI
The Effects of Augmented Training Dataset on Performance of Convolutional Neural Networks in Face Recognition System
TL;DR: Non-real-time face recognition has been performed by training with new augmented dataset of each picture with many features of a small-scale company, using the Convolutional Neural Network model.
Proceedings ArticleDOI
Turkish meaningful text generation with class based n-gram model
Mehmet Ali Kutlugun,Yahya Sirin +1 more
TL;DR: In this study, it is aimed to generate meaningful new Turkish sentences using class-based n-gram model from the sentences in the source data set using a trigram model.
Journal ArticleDOI
Augmenting the training database with the method of gradual similarity ratios in the face recognition systems
Mehmet Ali Kutlugun,Yahya Sirin +1 more
TL;DR: In this paper , a method called Graded Similarity Rates (GSR) was proposed to remove data that could disrupt class integrity from a pre-trained deep neural network model.
Proceedings ArticleDOI
A novel approach improvement framework for text to speech synthesis
Mehmet Ali Kutlugun,Yahya Sirin +1 more
TL;DR: Text to speech applications are mostly used to extract interaction with the user in high-level multimedia tools and instead of synthesizing textual expressions as monotone, single sound form, it is aimed to be separated into species and sounded as different sound forms.
Journal ArticleDOI
Anlamlı ve Benzer Olmayan Türkçe Metinler Üretmek için N-Gram Yöntemi ile İstatistiksel ve Kural Tabanlı Yaklaşımın Birlikte Kullanımı
Yahya Sirin,Mehmet Ali Kutlugun +1 more
TL;DR: In this paper, a Trigram model has been proposed to generate Turkish sentences using class-based n-gram model from the sentences in the source data set and this model was developed for use with rule based approach.