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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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Institution of Engineering and Technology
2022
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| Subjects: | |
| 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 http://umpir.ump.edu.my/id/eprint/41957/ https://doi.org/10.1049/icp.2022.2484 |
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| Summary: | 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. |
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