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September 10, 2025Scientific Journal of Astana IT UniversityOpen Access

Audio-to-Text Translation for the Hard of Hearing: A Whisper Model-Based Study

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AAArypzhan AbenAhmet Yesevi University GKGulnur KazbekovaAhmet Yesevi University ZIZhuldyz IsmagulovaInstitute of Information and Computational Technologies

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Implication

Study evaluates Whisper model's effectiveness for transcription in noisy conditions for hearing-impaired individuals, suggesting significant advancements in accessibility.

Key Points

The Whisper model successfully transcribes audio recordings with background noise, improving accessibility for hearing impairments.
Training results showed a consistent reduction in loss values, confirming advancements in audio quality during transcription processes.
Using noise reduction techniques, including Librosa and Noisereduce, significantly improved the clarity of audio input for accurate transcription.
Research highlights the potential of machine learning technologies like Whisper to enhance communication access, indicating scope for future language applications.

Cite This Study

Aben et al. (2025) studied this question.

synapsesocial.com/papers/68c1ae6654b1d3bfb60e5fba https://doi.org/10.37943/22snok5872

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Implication

Study evaluates Whisper model's effectiveness for transcription in noisy conditions for hearing-impaired individuals, suggesting significant advancements in accessibility.

Audio-to-Text Translation for the Hard of Hearing: A Whisper Model-Based Study

Discussion

Member takes

Implication

Key Points

Cite This Study

Audio-to-Text Translation for the Hard of Hearing: A Whisper Model-Based Study

Discussion

Member takes

Implication

Key Points

Cite This Study

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