Morse Code-Enabled Speech Recognition for Individuals with Visual and Hearing Impairments
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914879444615168 |
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| author | Choudhury, Ritabrata Roy |
| author_facet | Choudhury, Ritabrata Roy |
| contents | The proposed model aims to develop a speech recognition technology for hearing, speech, or cognitively disabled people. All the available technology in the field of speech recognition doesn't come with an interface for communication for people with hearing, speech, or cognitive disabilities. The proposed model proposes the speech from the user, is transmitted to the speech recognition layer where it is converted into text and then that text is then transmitted to the morse code conversion layer where the morse code of the corresponding speech is given as the output. The accuracy of the model is completely dependent on speech recognition, as the morse code conversion is a process. The model is tested with recorded audio files with different parameters. The proposed model's WER and accuracy are both determined to be 10.18% and 89.82%, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14525 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Morse Code-Enabled Speech Recognition for Individuals with Visual and Hearing Impairments Choudhury, Ritabrata Roy Sound Artificial Intelligence Computation and Language Human-Computer Interaction Machine Learning The proposed model aims to develop a speech recognition technology for hearing, speech, or cognitively disabled people. All the available technology in the field of speech recognition doesn't come with an interface for communication for people with hearing, speech, or cognitive disabilities. The proposed model proposes the speech from the user, is transmitted to the speech recognition layer where it is converted into text and then that text is then transmitted to the morse code conversion layer where the morse code of the corresponding speech is given as the output. The accuracy of the model is completely dependent on speech recognition, as the morse code conversion is a process. The model is tested with recorded audio files with different parameters. The proposed model's WER and accuracy are both determined to be 10.18% and 89.82%, respectively. |
| title | Morse Code-Enabled Speech Recognition for Individuals with Visual and Hearing Impairments |
| topic | Sound Artificial Intelligence Computation and Language Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2407.14525 |