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Benefits of Human Transcription

Human transcriptionists continue to offer the highest accurate transcription services despite breakthroughs in speech recognition technologies. When transcribing telephone conversations, generally available voice recognition software has an error rate of close to 12%, compared to an average human transcriptionist mistake rate of 4% and some transcriptionist error rate of 1%.

Save yourself the danger and choose London Transcription's 100% human transcription if you require precise and expert-calibre services. For this reason, a lot of people are still reluctant to embrace the use of the available speech recognition technologies.

Although the world's largest and most intelligent artificial intelligence organisations have been working for years to improve automatic speech-to-text transcription, they can still only achieve an accuracy of up to 88% at most. Companies that use human transcriptionists are able to achieve accuracy levels of 99–100%.

Why Using Human Transcription Is Preferred

The list of talks that a business might need to have transcribed includes interviews, meetings, and podcasts. There are two alternatives available when it comes to transcription: manual transcription and automatic transcription.

Both options have advantages, but most people discover that human transcribing best suits their needs. Even as automatic transcription is taking shape around the world, people are still yet to fully embrace the services.

Human transcription still has an edge over automatic transcription. So why is human transcribing frequently favoured even if many processes today are automated?

Here are a few of the main justifications to take into account human transcribing for any forthcoming initiatives.

1. People can handle noise in the background

The capacity of human transcriptionists to naturally filter out background noise is their largest advantage over automated transcribe tools. The best practice is always to display the audio file that is the clearest possible.

But even when there is background noise, a human may still produce a reliable transcript. Background noise might be a problem for automated transcription systems. As a result, the audio file may be completely rejected or result in erroneous transcripts.

This is one of the most significant benefits associated with human transcription considering the likelihood of the presence of background noise.

2. More Accurate

Of course, when someone needs something transcribed, accuracy is frequently their top concern. There cannot be errors that could endanger the information in key transcripts. Automated transcriptions are trustworthy, yet even robots can make mistakes.

When transcribing audio and video data, humans can give the work the amount of attention that machines cannot. To assist prevent errors and guarantee the accuracy, human transcribers often proofread each transcript. This explains why human transcription is still very much sort after where accuracy is a priority.

3. To achieve 99% accuracy, automated transcription services need a hybrid model

Numerous services have switched to a hybrid transcription paradigm due to these problems and others that automated transcription software runs into. After using speech recognition software to create a transcript, a person must edit and proofread it.

This model was created to save time and decrease costs. Results, however, are frequently less accurate because the automated transcription software's transcript can significantly differ from the audio.

4. Humans are Able to Understand Different Dialects and Accents

Accents and dialects present another challenge for automatic transcription software. Speech recognition software struggles with accents that are different from one another or even dialects that it is unable to identify.

But people are constantly exposed to different accents and dialects. No matter the speaker's accent, human transcriptionists have an advantage thanks to this exposure and their inherent capacity for adaptation.

There are a number of transcription services that provide top-notch transcription services on audio files with different accents in an increasingly international marketplace.

5. Verbatim transcriptions

In a variety of circumstances, verbatim transcription is required, which entails recording dialogue verbatim. Nothing is omitted during verbatim transcription, not even filler words, grammatical mistakes, or other gaffes made by the speaker that can be removed during some transcriptions.

Human transcription is the best service to use when verbatim transcripts are required. When verbatim transcription is required, human transcribers won't overlook any details, giving businesses peace of mind that everything is covered.

6. Homophones are Easily Differentiated by Humans (To, Too, Two)

Searching for homophone faults is one of the quickest ways to determine whether a transcript was produced by a computer. Homophones are two or more words that sound alike yet have distinct meanings, histories, or spellings.

To determine the most likely word to use, speech recognition software must use sentence structure. This frequently results in errors. Let's use the example of a doctor who was impatient and frustrated with someone.

Based on the context, a human transcriptionist would write, "The doctor lost his patience." The phrase "The doctor lost his patients" would probably be typed by an automated transcriptionist.

7. The Ability of Humans to Recognize Any Number of Individual Speakers

When there are more than two speakers present, automated transcription software frequently struggles to distinguish between different speakers and provide reliable transcripts. Anyone who wants to transcribe audio with three or more speakers, such as for a business meeting, may find this to be quite difficult.

However, even when speakers sound the same, humans are still able to recognise and distinguish between different speakers. Automated transcription software will have difficulty not just recognising several speakers in the first place, but also accurately following those voices throughout the recording.

Big tech businesses are investing billions of dollars in voice recognition systems, which is accelerating the development of AI speech and transcription technology. As additional training data becomes available in the upcoming years, significant advancements in automated voice recognition and transcription will be achieved.

Right now, human transcribers and speech-to-text technology work together flawlessly to produce high-quality transcriptions by combining accuracy and speed. In other words, post-editing can be considered a workaround for AI's flaws.


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