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Automatic Speech Recognition, or ASR, has increased in popularity over the years as it demonstrates the use of artificial intelligence in voice assistance. Learning never stops, and humans always find a way to seek new improvements to educate themselves. This study focuses on utilizing the practicality of technology to process human speech into readable text through Automatic Speech Recognition and Speech-to-Text Recognition or STR. The developed application integrates AI into the collegiate course Great Books, which introduces students to literary genres and pieces. The web-based application integrates OpenAI (Whisper API) for transcription and Google Translate API for translation. Functional testing, System Usability Score (SUS), and User Experience Questionnaire (UEQ) were facilitated to ensure the developed software is widely applicable to its purpose. The result of SUS is highly acceptable, while the UEQ scale shows that the highest criterion is perspicuity with a mean of 1.121, while efficiency is 0.95. This means that the web-based transcription and translation applications are clear and easy to understand and perform with competency.

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SpeakPlease: A Web-Based Speech-to-Text Transcription Using Automatic Speech Recognition for Collegiate Learning Materials

  • 会议论文

  • 发表日期：

    2025年

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  • Mary Jane C. Samonte,
  • Jeremi An Armado,
  • Fernando G. Bustos,
  • Nicole Angelique M. Laysico

摘要

Automatic Speech Recognition, or ASR, has increased in popularity over the years as it demonstrates the use of artificial intelligence in voice assistance. Learning never stops, and humans always find a way to seek new improvements to educate themselves. This study focuses on utilizing the practicality of technology to process human speech into readable text through Automatic Speech Recognition and Speech-to-Text Recognition or STR. The developed application integrates AI into the collegiate course Great Books, which introduces students to literary genres and pieces. The web-based application integrates OpenAI (Whisper API) for transcription and Google Translate API for translation. Functional testing, System Usability Score (SUS), and User Experience Questionnaire (UEQ) were facilitated to ensure the developed software is widely applicable to its purpose. The result of SUS is highly acceptable, while the UEQ scale shows that the highest criterion is perspicuity with a mean of 1.121, while efficiency is 0.95. This means that the web-based transcription and translation applications are clear and easy to understand and perform with competency.