Transcribing in the digital age: qualitative research practice utilizing intelligent speech recognition technology

Area of Science:

• Social Sciences
• Computer Science
• Information Science

Background:

• Qualitative research relies on accurate data transcription from interviews, focus groups, and fieldwork notes.
• Manual transcription is often time-consuming, expensive, and labor-intensive, requiring transparent methodologies.
• The digital revolution presents opportunities to enhance data collection methods in research.

Purpose of the Study:

• To explore the application and implications of intelligent speech recognition (ISR) technology in qualitative data transcription.
• To describe current challenges and opportunities associated with using ISR for transcribing audio data.
• To present a practical application of ISR-based transcription methodology.

Main Methods:

• Review of current transcription practices in qualitative research.
• Analysis of the capabilities and limitations of intelligent speech recognition technology for audio-to-text conversion.
• Description of a novel methodology integrating ISR into the transcription workflow.
• Presentation of a case study or application example.

Main Results:

• ISR technology has the potential to significantly reduce transcription time and costs.
• Challenges include accuracy in diverse audio conditions and the need for human oversight.
• Opportunities lie in improved efficiency, consistency, and accessibility of qualitative data.
• The presented methodology demonstrates a viable application of ISR for audio data transcription.

Conclusions:

• Intelligent speech recognition technology offers a promising advancement for qualitative research data transcription.
• Adoption of ISR can enhance research efficiency and data accessibility, though careful implementation is necessary.
• Further research and development are needed to optimize ISR tools for diverse qualitative research contexts.