Development of a neural network­based transcription method

Abstract

In today's ever-evolving digital era, the utilization of audio and video materials has become increasingly prevalent in various spheres, including educational sectors, research initiatives, technological developments, and daily communication. This trend is driven by the convenience and efficiency of these mediums, offering quick access to information without the distractions often associated with more traditional methods like manual writing or typing, especially in scenarios where such activities are impractical, such as when traveling in a vehicle. However, this shift towards audio-visual content has introduced the challenge of converting these dynamic formats into text for a range of purposes. This need arises for several reasons. Firstly, editing and refining spoken content is often easier when it's transformed into a written format, allowing for a more thorough review and adjustment process. Secondly, in situations where individuals prefer or require written documentation — for instance, in academic or professional settings — transcription becomes a critical tool. Additionally, the clarity of audio and video content can vary greatly, with factors like diction, accent, background noise, and recording quality affecting the comprehensibility of the material. To address these challenges, transcription — the process of translating audio and video content into text — has become a valuable solution. It involves a meticulous process of listening, interpreting, and typing out the content, ensuring that the essence and nuances of the original material are accurately captured. This task demands not only attention to detail but also a deep understanding of the context and subject matter to ensure precision and reliability. In the realm of technology, advancements such as neural networks have revolutionized the transcription process. Neural networks, complex algorithmic structures that emulate human brain functioning, can learn, adapt, and process diverse types of data. In transcription, they are employed to recognize and interpret various speech patterns, accents, and languages, significantly enhancing the accuracy and speed of converting spoken words into written text. This integration of advanced technology in transcription not only streamlines the process but also opens new possibilities for accessibility, research, and data analysis, making it an indispensable tool in the modern world.