8 Reasons Why Machine Transcriptions Will Never Replace Humans

8 Reasons Why Human Transcribers Will Never Be Replaced by Machine Transcriptions

Technology continues to take over manual work that previously required human energy and time to complete. Nowadays, humans are willing to delegate tedious tasks to machines and consequently save on overhead costs, training times and get more jobs done in less time.

In machine transcription, however, that has not been the case. There are all sorts of speech recognition software on the market, but none can currently match the quality of manual transcription. The main reason for acquiring an artificially intelligent voice recognition software is to increase productivity by getting more work done in half the time.

Unfortunately, as you’ll discover in this article, machine transcription can be more of a curse than a blessing

1. Audio Quality

Machine transcription converts spoken word into written word but it cannot differentiate a noisy background from the actual speech that needs transcribing. To use machine transcription with any chance of success, you have to be in a very quiet environment. If you’re in a noisy place, the machine will transcribe the background noise as well, and you’ll end having a document that reads complete gibberish.

Human transcribers, on the other hand, can differentiate speech from background noise. Should the quality of your audio be somewhat low, you’ll still get a high-quality transcription that expresses your message as it was intended. The great thing is a human transcriber can also ask questions, so if there’s anything they’re unsure of, they will clarify it with you instead of making mistakes.

2. Multiple Speakers

Conversations among numerous speakers can quickly overwhelm a transcription robot. It gets worse if the speakers have different accents or are in a noisy place. Devices are not smart enough to recognise the exchange of information amongst people, and consequently, it transcribes as one long prose.

Professional human transcribers can tell a when there are multiple speakers. Their intelligence can sense different accents, voice tones and consequently decipher them as such.

3. Context & Meaning

Humans are versatile, they have experience and training on subject matters, and when they’re transcribing, their knowledge, experience, and emotional intelligence help them create documents that are accurate and read correctly.

Machines simply transcribe; they cannot understand the context or the deeper meaning of an audio file. In non-verbatim files, machines are particularly ineffective as they can’t filter out additional unnecessary words and can only make an exact replica of what is in the audio file. This leads to post-editing requirements, and consequently more work for the end user.

4. Homonyms

A machine writes out words exactly as they sound. Since they do not understand the meaning, they may spell out homonyms that are actually different from those used in the context.

Humans follow the conversation and understand the meaning of the words spoken. As a result, they spell words to fit the setting even when several words could sound the same.

5. People and Place Names

A machine needs constant training to understand the various nouns. If a name is not present in its dictionary, it may autocorrect the word to say something else entirely. It may be close to how it sounds but it does not recognise it as the name of a place or a person.

Humans have research skills that they use when faced with unfamiliar nouns and they are therefore unlikely to misrepresent a noun as anything else. Again, humans also have the ability to check with the end-user.

6. Accents

Different languages can have many different accents, some that are vastly different. For a robot to transcribe correctly, it not only needs to be trained on all accents but it also needs to be able to determine the accent being used as soon as it’s heard. When accents affect the pronunciation of a word, a machine may spell them out to the closest resembling word in their internal dictionary which could be lead to an incorrect interpretation and subsequent invalidity of an entire document.

Humans understand different accents and thus can understand the differences between them. Different accents have different spellings for various words, and only a human can get understand this immediately and work accordingly.

7. Dialects & Slang

When a machine receives an unfamiliar word, it immediately autocorrects it to the closest meaning or skips the word altogether stating that it’s inaudible. Different societies have different words in slang that robots cannot understand unless they’re programmed. Humans can immediately determine slang by researching the word and spelling it out correctly to fit the context of the document.

8. Punctuation & Grammar

Most machines do a fairly good job on punctuation, but none can beat the perfection of a human transcriber. A machine may interpret a pause to mean the end of a sentence when in essence, the speaker may be trying to come up with the next part of the same sentence or even sipping a drink, for example.

Conclusion

It takes a human to understand another human. A machine can learn, but it cannot reach the perfection of a human transcriber. A human can even understand a message through a facial expression or body language while a machine may interpret that as silence. For organisations that value quality over quantity, human transcription is definitely the right choice.

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