Which file format is commonly used for machine-readable transcripts?

Which file format is commonly used for machine-readable transcripts?

• A. PDF
• B. WAV
• C. XML
• D. TXT

Answer: (C)

When a transcript needs to be processed by software, structure matters as much as the text itself. XML provides a flexible, hierarchical markup system that lets you tag each piece of speech with metadata like who spoke and when, and to nest related elements within a clear framework. This makes it straightforward for programs to parse the content, extract speaker and timestamp information, search the transcript, and align it with audio or video. In practice, you’d encode the transcript with elements such as an utterance that includes attributes or child elements for the speaker and time, for example: a line of dialogue tagged with the speaker and the exact time it was spoken. This kind of tagging enables automated workflows, data exchange between systems, and reliable data extraction. Other formats shown are less suitable for long-term machine processing of transcripts. A plain text file is easy to read but lacks inherent structure, which means extra conventions would be needed to interpret who said what and when. PDF preserves layout but isn’t easy to parse for data without sophisticated tools. WAV is an audio format, not a transcript, so it doesn’t contain the written content itself. XML is the most effective option among these for representing transcripts in a way that software can readily use.

When a transcript needs to be processed by software, structure matters as much as the text itself. XML provides a flexible, hierarchical markup system that lets you tag each piece of speech with metadata like who spoke and when, and to nest related elements within a clear framework. This makes it straightforward for programs to parse the content, extract speaker and timestamp information, search the transcript, and align it with audio or video.

In practice, you’d encode the transcript with elements such as an utterance that includes attributes or child elements for the speaker and time, for example: a line of dialogue tagged with the speaker and the exact time it was spoken. This kind of tagging enables automated workflows, data exchange between systems, and reliable data extraction.

Other formats shown are less suitable for long-term machine processing of transcripts. A plain text file is easy to read but lacks inherent structure, which means extra conventions would be needed to interpret who said what and when. PDF preserves layout but isn’t easy to parse for data without sophisticated tools. WAV is an audio format, not a transcript, so it doesn’t contain the written content itself. XML is the most effective option among these for representing transcripts in a way that software can readily use.