Development of audio-visual speech recognition using deep-learning technique
Deep learning is a technique with artificial intelligent (AI) that simulate humans’ learning behavior. Audio-visual speech recognition is important for the listener understand the emotions behind the spoken words truly. In this thesis, two different deep learning models, Convolutional Neural Network...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2022
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/37244/1/Development%20of%20audio%20visual%20speech%20recognition.pdf |
| _version_ | 1825814894173224960 |
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| author | How, Chun Kit Mohd Khairuddin, Ismail Mohd Razman, Mohd Azraai Anwar, P. P. Abdul Majeed Mohd Isa, Wan Hasbullah |
| author_facet | How, Chun Kit Mohd Khairuddin, Ismail Mohd Razman, Mohd Azraai Anwar, P. P. Abdul Majeed Mohd Isa, Wan Hasbullah |
| author_sort | How, Chun Kit |
| collection | UMP |
| description | Deep learning is a technique with artificial intelligent (AI) that simulate humans’ learning behavior. Audio-visual speech recognition is important for the listener understand the emotions behind the spoken words truly. In this thesis, two different deep learning models, Convolutional Neural Network (CNN) and Deep Neural Network (DNN), were developed to recognize the speech’s emotion from the dataset. Pytorch framework with torchaudio library was used. Both models were given the same training, validation, testing, and augmented datasets. The training will be stopped when the training loop reaches ten epochs, or the validation loss function does not improve for five epochs. At the end, the highest accuracy and lowest loss function of CNN model in the training dataset are 76.50% and 0.006029 respectively, meanwhile the DNN model achieved 75.42% and 0.086643 respectively. Both models were evaluated using confusion matrix. In conclusion, CNN model has higher performance than DNN model, but needs to improvise as the accuracy of testing dataset is low and the loss function is high. |
| first_indexed | 2024-03-06T13:05:20Z |
| format | Article |
| id | UMPir37244 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T13:05:20Z |
| publishDate | 2022 |
| publisher | Penerbit UMP |
| record_format | dspace |
| spelling | UMPir372442023-03-09T03:50:23Z http://umpir.ump.edu.my/id/eprint/37244/ Development of audio-visual speech recognition using deep-learning technique How, Chun Kit Mohd Khairuddin, Ismail Mohd Razman, Mohd Azraai Anwar, P. P. Abdul Majeed Mohd Isa, Wan Hasbullah TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Deep learning is a technique with artificial intelligent (AI) that simulate humans’ learning behavior. Audio-visual speech recognition is important for the listener understand the emotions behind the spoken words truly. In this thesis, two different deep learning models, Convolutional Neural Network (CNN) and Deep Neural Network (DNN), were developed to recognize the speech’s emotion from the dataset. Pytorch framework with torchaudio library was used. Both models were given the same training, validation, testing, and augmented datasets. The training will be stopped when the training loop reaches ten epochs, or the validation loss function does not improve for five epochs. At the end, the highest accuracy and lowest loss function of CNN model in the training dataset are 76.50% and 0.006029 respectively, meanwhile the DNN model achieved 75.42% and 0.086643 respectively. Both models were evaluated using confusion matrix. In conclusion, CNN model has higher performance than DNN model, but needs to improvise as the accuracy of testing dataset is low and the loss function is high. Penerbit UMP 2022-06 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/37244/1/Development%20of%20audio%20visual%20speech%20recognition.pdf How, Chun Kit and Mohd Khairuddin, Ismail and Mohd Razman, Mohd Azraai and Anwar, P. P. Abdul Majeed and Mohd Isa, Wan Hasbullah (2022) Development of audio-visual speech recognition using deep-learning technique. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 4 (1). pp. 88-95. ISSN 2637-0883. (Published) https://doi.org/10.15282/mekatronika.v4i1.8625 https://doi.org/10.15282/mekatronika.v4i1.8625 |
| spellingShingle | TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures How, Chun Kit Mohd Khairuddin, Ismail Mohd Razman, Mohd Azraai Anwar, P. P. Abdul Majeed Mohd Isa, Wan Hasbullah Development of audio-visual speech recognition using deep-learning technique |
| title | Development of audio-visual speech recognition using deep-learning technique |
| title_full | Development of audio-visual speech recognition using deep-learning technique |
| title_fullStr | Development of audio-visual speech recognition using deep-learning technique |
| title_full_unstemmed | Development of audio-visual speech recognition using deep-learning technique |
| title_short | Development of audio-visual speech recognition using deep-learning technique |
| title_sort | development of audio visual speech recognition using deep learning technique |
| topic | TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/37244/1/Development%20of%20audio%20visual%20speech%20recognition.pdf |
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