Casson Qin¹ and Jack Wagner², ¹USA, ²California State Polytechnic University, USA

This study evaluated the accuracy and reliability of Voice Note Taking, a technology designed to transcribe spoken language and support note-taking. The experiment analyzed the transcription accuracy and word definition selection feature of Voice Note Taking using a series of audio files featuring individuals speaking in English in different settings. The results showed that Voice Note Taking is reliable and accurate, with an overall transcription accuracy rate of 87.81%. However, the study identified room for improvement, particularly in improving accuracy in noisy environments and developing more sophisticated algorithms for word definition selection. Future research could explore the integration of advanced natural language processing techniques to improve the accuracy of word definition selection, including leveraging machine learning algorithms to recognize the specific context and meaning of words. Several previous studies have shown the potential of mobile note-taking apps to enhance student achievement, satisfaction, and accessibility, suggesting further research in this area. Overall, this study highlights the strengths and limitations of Voice Note Taking and provides insight into potential areas for future development.

Natural Language Processing , Speech Recognition, Note Taking, Mathematics