Transcription services can take important audio and video content and turn it into a convenient and accessible text file that is easy to distribute, search, and save for future needs. There are two types of transcription services that you’ll come across. Full verbatim transcription (also known as strict verbatim or verbatim transcription) and clean verbatim transcription (also known as non-verbatim or intelligent verbatim transcription). Let’s examine how these two transcription types differ and what factors can influence the quality of a transcription project.
Full Verbatim Transcription
When you undergo a full verbatim transcription, the goal is to do a word-for-word transcription of the spoken language. This includes filler words like “um”, incomplete sentences, and even sounds like throat clearing and laughter.
Generally, full verbatim transcription is used when the behavior and reactions of the person(s) being recorded, such as in police investigations, court cases, and market research studies. This is the most expensive type of transcription to undertake as it typically takes the longest to perform. Let’s look at an example of what a full verbatim transcription can look like.
While that type of text is difficult to read, you can see how the filler words and pauses show uncertainty on the speaker’s part. While pauses and mispeaking in a business presentation may be chalked up to nerves, in a police investigation those signs of uncertainty might be a lot more meaningful.
Clean Verbatim Transcription
Clean verbatim transcription filters the spoken language a bit, as the main purpose of this type of transcription is to extract the meaning of what was being said. During a clean verbatim transcription, filler words, pauses, and sounds like coughing or sighing will be omitted. The transcriber may even edit the text a bit to correct sentences for grammar or to eliminate irrelevant words or sentences.
Clean verbatim transcription is used when the meaning of what was said is more important than the exact wording that occurred, such as when transcribing business presentations or medical diagnoses. In these cases understanding the text and being able to easily read it is more important than the reactions of those that were speaking and it’s not helpful to have every pause or self-correction noted. Let’s look at the same transcribed text from early, but from a clean verbatim perspective.
You can see how in this example, the text is cleaned up and all the key information is clarified.
Where Timestamps and Audio Quality Come Into Play
With either type of transcription, two things you’ll need to take into consideration are how timestamps and audio quality can impact the final transcription product.
One way the transcriptionist can help deliver a quality product is by time-stamping the typed copy. This really comes in handy when managing a video transcription project as you can connect dialogue with the relevant visual sections of the file. Timestamps can be vital when dealing with foreign-language dialogue applied to video as it helps keep the spoken and visual elements of the file in sync — this also applies to subtitles. A transcriptionist must time-stamp the text version and depending on the purpose of the transcription, timestamps can be applied every one or two minutes or every time a new speaker starts talking, it really depends on the project’s unique requirements.
The quality of the audio file can also greatly impact a transcription project. Professional transcriptionists can only do so much if the file they have been given is poor quality. They need to be able to clearly understand what is being said so they can transcribe it properly. You’ll want to consult your transcriptionist first to make sure you’re both on the same page about what you can achieve. There are some workarounds for small quality issues. If the bad quality audio only happens occasionally in an audio file — for example an ambulance on the street for just 10 seconds — the transcriptionists will add “[unintelligible]” and continue transcribing once the speech becomes intelligible again. But if the whole audio is difficult to understand, then the quality of the transcription will be impacted.