Image approach to english digits recognition using deep learning
Despite good progress in speech recognition, various challenges still exist due to differences in how they speak, age, gender, emotions, and dialects when perceived by the ear. There is a proverb “I hear, and I forget; I see, and I remember”. The image would be another solution to recognize what we...
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Institution of Engineering and Technology
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/41957/1/Image%20approach%20to%20english%20digits%20recognition%20using%20deep%20learning.pdf http://umpir.ump.edu.my/id/eprint/41957/2/Image%20approach%20to%20english%20digits%20recognition%20using%20deep%20learning_ABS.pdf |
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| author | Fatin Nur Amalina, Zainol Mohd Zamri, Ibrahim |
| author_facet | Fatin Nur Amalina, Zainol Mohd Zamri, Ibrahim |
| author_sort | Fatin Nur Amalina, Zainol |
| collection | UMP |
| description | Despite good progress in speech recognition, various challenges still exist due to differences in how they speak, age, gender, emotions, and dialects when perceived by the ear. There is a proverb “I hear, and I forget; I see, and I remember”. The image would be another solution to recognize what we hear. The main objective of this paper is to investigate the graphic method to learn digit English using the Deep Learning technique. In this work, Mel-frequency cepstral coefficients (MFCC) in the form of an image will be used as input to the system. Convolutional neural network (CNN) will be used to extract features from the image and an artificial neural network (ANN) will be used to classify those features into 10-digit English classes. By using the Speech Command dataset, the performance of the system will be compared with a conventional method that uses MFCC features in the form of a signal. The experiments showed that the image approach improves the recognition rate from 49% to 84%. It can be concluded that image approach can be used as an alternative method for digit recognition. |
| first_indexed | 2024-09-25T03:51:33Z |
| format | Conference or Workshop Item |
| id | UMPir41957 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-09-25T03:51:33Z |
| publishDate | 2022 |
| publisher | Institution of Engineering and Technology |
| record_format | dspace |
| spelling | UMPir419572024-08-30T00:24:01Z http://umpir.ump.edu.my/id/eprint/41957/ Image approach to english digits recognition using deep learning Fatin Nur Amalina, Zainol Mohd Zamri, Ibrahim T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Despite good progress in speech recognition, various challenges still exist due to differences in how they speak, age, gender, emotions, and dialects when perceived by the ear. There is a proverb “I hear, and I forget; I see, and I remember”. The image would be another solution to recognize what we hear. The main objective of this paper is to investigate the graphic method to learn digit English using the Deep Learning technique. In this work, Mel-frequency cepstral coefficients (MFCC) in the form of an image will be used as input to the system. Convolutional neural network (CNN) will be used to extract features from the image and an artificial neural network (ANN) will be used to classify those features into 10-digit English classes. By using the Speech Command dataset, the performance of the system will be compared with a conventional method that uses MFCC features in the form of a signal. The experiments showed that the image approach improves the recognition rate from 49% to 84%. It can be concluded that image approach can be used as an alternative method for digit recognition. Institution of Engineering and Technology 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41957/1/Image%20approach%20to%20english%20digits%20recognition%20using%20deep%20learning.pdf pdf en http://umpir.ump.edu.my/id/eprint/41957/2/Image%20approach%20to%20english%20digits%20recognition%20using%20deep%20learning_ABS.pdf Fatin Nur Amalina, Zainol and Mohd Zamri, Ibrahim (2022) Image approach to english digits recognition using deep learning. In: IET Conference Proceedings. 2022 Engineering Technology International Conference, ETIC 2022 , 7 - 8 September 2022 , Kuantan, Virtual. pp. 6-11., 2022 (22). ISSN 2732-4494 ISBN 978-183953782-0 (Published) https://doi.org/10.1049/icp.2022.2484 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Fatin Nur Amalina, Zainol Mohd Zamri, Ibrahim Image approach to english digits recognition using deep learning |
| title | Image approach to english digits recognition using deep learning |
| title_full | Image approach to english digits recognition using deep learning |
| title_fullStr | Image approach to english digits recognition using deep learning |
| title_full_unstemmed | Image approach to english digits recognition using deep learning |
| title_short | Image approach to english digits recognition using deep learning |
| title_sort | image approach to english digits recognition using deep learning |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/41957/1/Image%20approach%20to%20english%20digits%20recognition%20using%20deep%20learning.pdf http://umpir.ump.edu.my/id/eprint/41957/2/Image%20approach%20to%20english%20digits%20recognition%20using%20deep%20learning_ABS.pdf |
| work_keys_str_mv | AT fatinnuramalinazainol imageapproachtoenglishdigitsrecognitionusingdeeplearning AT mohdzamriibrahim imageapproachtoenglishdigitsrecognitionusingdeeplearning |