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docsummarization

Extractive text summarization on Iranian News Dataset. The Dataset used Asriran, Roozno, Tabnak, Mashregh, And Sobhanehonline news.

dsr

Throughout history, Altough there has been significant research in the field of speech recognition, there are still some unsolved distant speech recognition (DSR) challenges, e.g., reverberation and background noise; hence there is a need for more robust speech recognizers. An approach to overcome the mentioned problems could be robust acoustic modeling in DSR. Yet, there has not been a classical/deep learning method to make the acoustic model robust against the aforementioned problems all at once. In the thesis, in order to dereverberate the input sound, we have employed weighted- prediction-error (WPE) algorithm and asymmetric-context-windows (ACW) method. Furthermore, in order to improve robustness and accuracy of multi-channel DSR and audio source direction finding, we have utilized an existing hidden Markov model-bidirectional quaternion long short-term memory (HMM-BQLSTM) hybrid acoustic model. Using four microphone inputs, the quaternion nature of BQLSTM neural network allows us to learn inter- and intra- structural dependencies. Additionally, the BQLSTM can learn long-term time domain dependencies with the help of its recurrent layers.

git-ninjas

ir-news-dataset

Iranian news dataset was prepared that crawled 4965 news from the websites of news agencies for developing extractive document/text summarization systems.

shessam

Config files for my GitHub profile.

shessam.github.io

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