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Das, Amit Kumar
- Implementation of Recurrent Neural Network with Language Model for Automatic Articulation Identification System in Bangla
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Authors
Affiliations
1 Department of Computer Science & Engineering, East West University, Dhaka, BD
2 Department of Computer Science & Engineering, East West University, Dhaka, Bangladesh, BD
1 Department of Computer Science & Engineering, East West University, Dhaka, BD
2 Department of Computer Science & Engineering, East West University, Dhaka, Bangladesh, BD
Source
International Journal of Advanced Networking and Applications, Vol 12, No 6 (2021), Pagination: 4800-4808Abstract
To nudge the state of the art of human-machine interacting applications, research in speech recognition systems has progressively been examining speech-to-text synthesis, but implementation has been done to minimal languages. Although the Bengali language has not been much of an object of interest, we present the automatic speech recognition (ASR) system solely based on this particular language since around 16% of the world’s population speak Bengali. It has been a demanding task to implement Bengali ASR because it consists of diacritic characters. We conduct a series of preprocessing and feature selection methods along with a convolutional neural net model in consideration of an automatic verbal communication recognition system. Furthermore, the researchers compared this method to a recurrent neural network that is based on an LSTM network and a vast data file of Google Inc. Investigation of these two models indicates such as the recurrent neural net outperforms the convolutional neural net: the former benefits from combining connectionist temporal classification (CTC) and language model (LM). A quantitative analysis of the output shows that the word error rate and validation loss can be affected by variation in dropout values. It also shows that the parameters are also affected by clean and augmented data.Keywords
Convolutional Neural Network, CTC, Word Error Rate, Edit Distance, Augmented Data, Test Loss, Validation Loss, Clean Data, Graphical User InterfaceFull Text
References
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- O. F. Rakib, S. Akter, M. A. Khan, A. K. Das and K. M. Habibullah, "Bangla Word Prediction and Sentence Completion Using GRU: An Extended Version of RNN on Ngram Language Model," 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI), 24-25 December, Dhaka.
- E. A. Emon, S. Rahman, J. Banarjee, A. K. Das and T. Mittra, "A Deep Learning Approach to Detect Abusive Bengali Text," 2019 7th International Conference on Smart Computing & Communications (ICSCC), Sarawak, Malaysia, Malaysia, 2019.
- M. M. Hossain, M. F. Labib, A. S. Rifat, A. K. Das and M. Mukta, "Auto-correction of English to Bengali Transliteration System using Levenshtein Distance," 2019 7th International Conference on Smart Computing & Communications (ICSCC), Sarawak, Malaysia, Malaysia, 2019.
- M. D. Drovo, M. Chowdhury, S. I. Uday and A. K. Das, "Named Entity Recognition in Bengali Text Using Merged Hidden Markov Model and Rule Base Approach," 2019 7th International Conference on Smart Computing & Communications (ICSCC), Sarawak, Malaysia, Malaysia, 2019.
- E. Biswas and A. K. Das, "Symptom-Based Disease Detection System In Bengali Using Convolution Neural Network," 2019 7th International Conference on Smart Computing & Communications (ICSCC), Sarawak, Malaysia, Malaysia, 2019.
- A. K. Das, A. Ashrafi and M. Ahmmad, "Joint Cognition of Both Human and Machine for Predicting Criminal Punishment in Judicial System," 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), Singapore, 2019, pp. 36-40.
- R. A. Tuhin, B. K. Paul, F. Nawrine, M. Akter and A. K. Das, "An Automated System of Sentiment Analysis from Bangla Text using Supervised Learning Techniques," 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), Singapore, 2019, pp. 360-364.
- J. Islam, M. Mubassira, M. R. Islam and A. K. Das, "A Speech Recognition System for Bengali Language using Recurrent Neural Network," 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), Singapore, 2019, pp. 73-76.
- T. F. Mumu, I. J. Munni, and A. K. Das, “Depressed People Detection from Bangla Social Media Status using LSTM and CNN Approach”, J. eng. adv., vol. 2, no. 01, pp. 41-47, Mar. 2021.
- M. T. Hossain, M. W. Hasan and A. K. Das, "Bangla Handwritten Word Recognition System Using Convolutional Neural Network," 2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM), 2021, pp. 1-8.
- Performance Analysis on Bangla Handwritten Digit Recognition using CNN and Transfer
Abstract Views :124 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, East West University, Dhaka, BD
1 Department of Computer Science and Engineering, East West University, Dhaka, BD
Source
International Journal of Advanced Networking and Applications, Vol 13, No 1 (2021), Pagination: 4809-4815Abstract
A computer can read and interpret intelligible handwritten input from sources like paper, photos, and other devices, known as Handwriting recognition (HWR). Besides, handwritten recognition is an interesting challenge in machine learning and deep learning. Because several strategies and approaches have been followed already to solve this challenge, machine learning and deep learning provided the best results.Handwritten digit recognition is a part of HWR. It is getting popular day by day because many applications could be made using this system like OCR, postal code recognition, license plate recognition, bank checks recognition, etc. Besides, the importance of recognizing the Bangla digit from the document is increasing. But the works available in Bangla handwritten digit recognition are very few.
Similarly, none of them are robust, and some of them are overfitted. Therefore, we need to make some improvements to this system considering its importance. This paper explores the presentation of transfer learning with the help of some best-in-class profound CNN strategies for the acknowledgment of manually written Bangla digits. It considers two deep CNN architectures, such as Mobile Net and Residual Network (Reset) based on performance and accuracy. This model was trained and tested with the CMATERdb dataset. The study suggests that transfer learning provides 97% accurate results, where traditional CNN provides 86-92 %.
Keywords
BangleHandwritten Digit, CNN, MobileNet, ResNet50, Transfer Learning.Full Text
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- O. F. Rakib, S. Akter, M. A. Khan, A. K. Das and K. M. Habibullah, "Bangla Word Prediction and Sentence Completion Using GRU: An Extended Version of RNN on Ngram Language Model," 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI), 24-25 December, Dhaka.
- E. A. Emon, S. Rahman, J. Banarjee, A. K. Das and T. Mittra, "A Deep Learning Approach to Detect Abusive Bengali Text," 2019 7th International Conference on Smart Computing & Communications (ICSCC), Sarawak, Malaysia, Malaysia, 2019.
- M. M. Hossain, M. F. Labib, A. S. Rifat, A. K. Das and M. Mukta, "Auto-correction of English to Bengali Transliteration System using Levenshtein Distance," 2019 7th International Conference on Smart Computing & Communications (ICSCC), Sarawak, Malaysia, Malaysia, 2019.
- M. D. Drovo, M. Chowdhury, S. I. Uday and A. K. Das, "Named Entity Recognition in Bengali Text Using Merged Hidden Markov Model and Rule Base Approach," 2019 7th International Conference on Smart Computing &Communications (ICSCC), Sarawak, Malaysia, Malaysia, 2019.
- E. Biswas and A. K. Das, "Symptom-Based Disease Detection System In Bengali Using Convolution Neural Network," 2019 7th International Conference on Smart Computing & Communications (ICSCC), Sarawak, Malaysia, Malaysia, 2019.
- A. K. Das, A. Ashrafi and M. Ahmmad, "Joint Cognition of Both Human and Machine for Predicting Criminal Punishment in Judicial System," 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), Singapore, 2019, pp. 36-40.
- R. A. Tuhin, B. K. Paul, F. Nawrine, M. Akter and A. K. Das, "An Automated System of Sentiment Analysis from Bangla Text using Supervised Learning Techniques," 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), Singapore, 2019, pp. 360-364.
- J. Islam, M. Mubassira, M. R. Islam and A. K. Das, "A Speech Recognition System for Bengali Language using Recurrent Neural Network," 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), Singapore, 2019, pp. 73-76.
- T. F. Mumu, I. J. Munni, and A. K. Das, “Depressed People Detection from Bangla Social Media Status using LSTM and CNN Approach”, J. eng. adv., vol. 2, no. 01, pp. 41-47, Mar. 2021.
- M. T. Hossain, M. W. Hasan and A. K. Das, "Bangla Handwritten Word Recognition System Using Convolutional Neural Network," 2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM), 2021, pp. 1-8.
- A. K. Das, A. A. Asif, A. Paul and M. N. Hossain, "Bangla hate speech detection on social media using attention-based recurrent neural network" Journal of Intelligent Systems 30, no. 1 (2021): 578-591.
- M. S. S.Khan, S. R. Rafa, and A. K. Das, "Sentiment Analysis on Bengali Facebook Comments To Predict Fan's Emotions Towards a Celebrity" Journal of Engineering Advancements,pp. 118-124, July 2021.
- M. Mubassira, and A. K. Das, “Implementation Of Recurrent Neural Network With Language Model For Automatic Articulation Identification System In Bangla”, International Journalof Advanced Networking and Applications - IJANA., vol. 12, no. 06, pp. 4800-4808, May-June, 2021.