publications

2018

1. ICBSLP

   Bangla Short Speech Commands Recognition Using Convolutional Neural Networks

   Shakil Ahmed Sumon, Joydip Chowdhury, Sujit Debnath, and 2 more authors

   In 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), 2018

   Abs DOI Bib

   Despite being one of the most widely spoken languages of the world, no significant efforts have been made in Bangla speech recognition. Speech recognition is a difficult task, particularly if the demand is to do so in noisy real-life conditions. In this study, Bangla short speech commands data set has been reported, where all the samples are taken in the real-life setting. Three different convolutional neural network (CNN) architectures have been designed to recognize those short speech commands. Mel-frequency cepstral coefficients (MFCC) features have been extracted from the audio files in one approach whereas only the raw audio files have been used in another CNN architecture. Lastly, a pre-trained model which is trained on a large English short speech commands data set has been fine-tuned by retraining on Bangla data set. Experimental results reveal that the MFCC model shows better accuracy in recognizing Bangla short speech commands where, surprisingly, the model predicting on raw audio data is very competitive. The models have shown proficiency in identifying single syllable words but encounter difficulties in recognizing multi-syllable commands.

   @inproceedings{8554395,
     author = {Ahmed Sumon, Shakil and Chowdhury, Joydip and Debnath, Sujit and Mohammed, Nabeel and Momen, Sifat},
     booktitle = {2018 International Conference on Bangla Speech and Language Processing (ICBSLP)},
     title = {Bangla Short Speech Commands Recognition Using Convolutional Neural Networks},
     year = {2018},
     volume = {},
     number = {},
     pages = {1-6},
     keywords = {Speech recognition;Mel frequency cepstral coefficient;Feature extraction;Data models;Convolutional neural networks;Hidden Markov models;Automatic Speech Recognition;Bangla Speech Recognition;Short Speech Commands;MFCC;Transfer learning;Convolutional neural network},
     doi = {10.1109/ICBSLP.2018.8554395},
   }

2. IS

   Early Detection of Glaucoma Using Fuzzy Logic in Bangladesh Context

   Nazmul Alam Diptu, Md. Asif Khan, Sujit Debnath, and 4 more authors

   In 2018 International Conference on Intelligent Systems (IS), 2018

   Abs DOI Bib

   Detecting Glaucoma at an early stage is very crucial to prevent irreversible vision loss. Glaucoma detection often requires advanced diagnosis which are not accessible to most of the hospitals in a developing country like Bangladesh. Therefore, detection of Glaucoma with the help of common and fewer tests would certainly improve the condition of the Glaucoma patients. In this paper, a method is devised to detect Glaucoma with the data obtained from two ophthalmological tests which are OCT and tonometry along with some other risk factors. The method uses Adaptive Neuro-Fuzzy Inference System (ANFIS) to train an Artificial Intelligence model which can make prediction about the presence of Glaucoma, the absence of it and whether the patient is suspected to have Glaucoma. Conventional Glaucoma detection techniques suggest the use of Intraocular pressure (IOP) as one of the primary parameters to detect Glaucoma. But by analyzing data of Glaucoma patients of Bangladesh, we observed that IOP is not the only significant factor for detecting Glaucoma. Our proposed method gives an accuracy of 81.25% using the ANFIS model.

   @inproceedings{8710490,
     author = {Diptu, Nazmul Alam and Khan, Md. Asif and Debnath, Sujit and Imam, Abdullah Al and Rakib, Al Mahadi Hasan and Ahmed Ador, Kazi Asfaq and Rahman, Rashedur M.},
     booktitle = {2018 International Conference on Intelligent Systems (IS)},
     title = {Early Detection of Glaucoma Using Fuzzy Logic in Bangladesh Context},
     year = {2018},
     volume = {},
     number = {},
     pages = {87-93},
     keywords = {Erbium;Magnetic resonance imaging;Hafnium;ANFIS;Early Glaucoma Detection;FIS;Glaucoma;OCT;Vision Loss},
     doi = {10.1109/IS.2018.8710490},
   }